New Group, New Act

After our first meeting today, it hit home to me that although the Phase I and phase II are similar, this is not going to be the exact same as phase I.  This may seem like an obvious statement but, what I mean by it is, in phase I, we all worked on how to figure out our programs and to do so we used a very small piece of text from Hamlet.  In Phase II however, we are left to figure out a slightly larger, but still not very big, excerpt from Hamlet using the tools that we became proficient with in Phase I.  To me, this is the same assignment as before, but somehow opposite to what we did before at the same time.  In a cursory analysis of Act II, the thing that stands out the most to me is the fact that Polonius is a puffed up, arrogant, windbag.  According to Voyeur’s summary chart, Polonius has a whopping 68 different moments when he speaks, that is not counting his total lines, just moments when he speaks.  This compared to Hamlet who only speaks on 49 separate occasions in this act shows that Polonius talks a lot.

In discussing Act II with my group, we came to a few conclusions about the act together, among them were the idea that it is an act that involves a lot of Polonius’ bumbling and screwing things up, it is also an event that has a lot private moments that are made public, such as Ophelia telling Polonius about her scene with Hamlet.  There is also a lot of Surveillance and observation of other characters which lead Polonius to his fatal habit of hiding behind tapestries.  In comparing the four most commonly occurring words in Act II, which are: Lord, Good, Shall, and Say; I have noticed that all though all four of these words appear together in places, the words: Shall and Say appear together the most often and that all four of them appear together in the fourth section of act II scene 1.

I do not yet know if this will be overly helpful or if it is merely interesting, but it is what has been done so far by me in Phase II of this project.

Unto the Analysis Once Again…

I go into the second phase of the analysis of Hamlet with a tad more anxiety than the first. I had grown comfortable in my tool group, what with the support and shared understanding of the TAPoR tool. But now I am thrust into another group with new people, while being expected to be the authority in how my tool works. I have to say that this is the source of fear; I don’t know how useful TAPoR will be along side any of the other tools, and I don’t know how much info I will be able to pull out of the text. However, I must move past this anxiety and proceed in my analysis.

I have been tasked in this phase to pick apart Act IV, something to which I am excited to do. I find act IV to be one of the more interesting acts, as it is here where things begin to come together. The characters begin to come face to face with situations they must deal with, full of anger and pent up emotion, which will lead into the fall of act V. My group’s intended route of progress is to begin comparing our interpretation of the act as we read it for ourselves and then compare what we pull from that open minded close reading to what our tools may give us.

To begin, In re-reading the act, a thought passes through my mind: I think that act IV may be seen in itself as a small, condensed version of the play; there are situations of confrontation, declarations of revenge, plotting, with everything to be wrapped up with a profound instance of the relation between madness and death.

The themes presented seem to be common enough to notice: there is reference to nature- as seen in relation to Ophelia, and even in the questioning of where Polonius’s body is- as well as references of blood and revenge, life and death and, of course, madness.

With a rough idea of the scene in my mind, I go to TAPoR and see what it can pull out. To be honest, at this early stage, there isn’t much. The list words tool (with which I use as a starting point) doesn’t show much in the way of pointing out the themes and references I notice while reading. In fact, the three most frequent words are a bit dull and have nothing to do with the things I found while reading:

Although, these results do give me a mood: it seems this act is one with much confrontation and planning, both building up towards the end of the act. What the distribution shows me is that these moods begin especially around (as is shown in the distribution of the words ’come’, ’let’ and ’shall’) the centre of the scene, when Hamlet has his conversation with Fortinbras which motivates him to take action.

As with my analysis of Act III.iii, TAPoR leaves something more to be desired with the analysis of the act. So far, it only gives me a limited view, having me miss everything that is being said if I only were to analyse using the tool. The frequent words used do not really give me a good insight: the distribution visual of the common words is lacking after the first five which are listed, leaving me lost in the significance of the other words used through the act.

My next steps will be to play with other tool in TAPoR, after going again through the text to pull out some more things to compare. It is my plan to break the act down into scenes and analyse them individually so that in the end I may stitch the individual results together to find something more significant than the general results I have pulled as of now. I realize that this may also be achieved by working with the other members of my group; with all the results we pull individually, we will be able to fill in the gaps each of us encounters with our tools, hopefully allowing us to have a successful insight into the whole act.

Act II

Hey, seeing as we couldn’t get the extract text for Tapor to work out for us, here is a copy of act two that can be uploaded to Tapor or Voyeur.

Act 2, Scene 1


A room in Plns’ house.

Enter PLNS and RNLDO.


Give him this money and these notes, Reynaldo.


I will, my lord.


You shall do marvellous wisely, good Reynaldo,

Before you visit him to make inquire

Of his behaviour.


My lord, I did intend it.


Marry, well said, very well said. Look you, sir,

Inquire me first what Danskers are in Paris,

And how, and who, what means, and where they keep,

What company, at what expense, and finding

By this encompassment and drift of question

That they do know my son, come you more nearer

Than your particular demands will touch it:

Take you, as ’twere some distant knowledge of him,

As thus, ‘I know his father and his friends,

And in part him’ – do you mark this, Reynaldo?


Ay, very well, my lord.


‘And in part him,’ but you may say, ‘not well:

But, if’t be he I mean, he’s very wild,

Addicted so and so’, and there put on him

What forgeries you please. marry, none so rank

As may dishonour him – take heed of that –

But, sir, such wanton, wild and usual slips

As are companions noted and most known

To youth and liberty.


As gaming, my lord?


Ay, or drinking, fencing, swearing,

Quarrelling, drabbing – you may go so far.


My lord, that would dishonour him.


‘Faith, as you may season it in the charge.

You must not put another scandal on him

That he is open to incontinency –

That’s not my meaning – but breathe his faults so


That they may seem the taints of liberty,

The flash and outbreak of a fiery mind,

A savageness in unreclaimed blood

Of general assault.


But, my good lord –


Wherefore should you do this?


Ay, my lord,

I would know that.


Marry, sir, here’s my drift –

And, I believe, it is a fetch of wit –

You laying these slight sallies on my son

As ’twere a thing a little soiled with working,

Mark you, your party in converse (him you would


Having ever seen in the prenominate crimes

The youth you breathe of guilty, be assured

He closes with you in this consequence:

‘Good sir’ (or so), or ‘friend’ or ‘gentleman’,

According to the phrase or the addition

Of man and country.


Very good, my lord.


And then, sir, does ‘a this, ‘a does –

what was I about to say? By the mass, I was about to

say something! where did I leave?


At ‘closes in the consequence’.


At ‘closes in the consequence’, ay, marry.

He closes thus: ‘I know the gentleman,

I saw him yesterday, or th’ other day,

Or then, or then, with such, or such; and, as you say

There was ‘a gaming; there o’ertook in’s rouse;

There falling out at tennis’, or perchance

‘I saw him enter such a house of sale’,

Videlicet a brothel, or so forth. See you now

Your bait of falsehood take this carp of truth,

And thus do we of wisdom and of reach,

With windlasses and with assays of bias,

By indirections find directions out:

So by my former lecture and advice

Shall you my son. You have me, have you not?


My lord, I have.


God buy ye; fare ye well.


Good my lord.


Observe his inclination in yourself.


I shall, my lord.


And let him ply his music.


Well, my lord.



Exit Rnldo.

Enter OPLA.

How now, Ophelia, what’s the matter?


O, my lord, my lord, I have been so affrighted.


With what, i’ the name of God?


My lord, as I was sewing in my closet

Lord Hamlet, with his doublet all unbraced,

No hat upon his head, his stockings fouled,

Ungartered and down-gyved to his ankle;

Pale as his shirt, his knees knocking each other,

And with a look so piteous in purport

As if he had been loosed out of hell

To speak of horrors, he comes before me.


Mad for thy love?


My lord, I do not know,

But truly I do fear it.


What said he?


He took me by the wrist and held me hard,

Then goes he to the length of all his arm

And with his other hand thus o’er his brow

He falls to such perusal of my face

As ‘a would draw it. Long stayed he so;

At last, a little shaking of mine arm

And thrice his head thus waving up and down,

He raised a sigh so piteous and profound

As it did seem to shatter all his bulk

And end his being. That done, he lets me go

And with his head over his shoulder turned

He seemed to find his way without his eyes

(For out o’ doors he went without their helps)

And, to the last bended their light on me.


Come, go with me: I will go seek the king.

This is the very ecstasy of love,

Whose violent property fordoes itself

And leads the will to desperate undertakings

As oft as any passions under heaven

That does afflict our natures. I am sorry –

What, have you given him any hard words of late?


No, my good lord, but as you did command,

I did repel his letters and denied

His access to me.


That hath made him mad.

I am sorry that with better heed and judgement

I had not quoted him. I feared he did but trifle

And meant to wrack thee – but, beshrew my jealousy –

By heaven it is as proper to our age

To cast beyond ourselves in our opinions

As it is common for the younger sort

To lack discretion. Come, go we to the king:

This must be known which, being kept close, might


More grief to hide than hate to utter love.




Act 2, Scene 2


A room in the castle.




Welcome, dear Rosencrantz and Guildenstern.

Moreover that we much did long to see you

The need we have to use you did provoke

Our hasty sending. Something have you heard

Of Hamlet’s transformation – so call it

Sith nor th’ exterior nor the inward man

Resembles that it was. What it should be

More than his father’s death, that thus hath put him

So much from th’ understanding of himself

I cannot dream of. I entreat you both

That, being of so young days brought up with him

And sith so neighboured to his youth and haviour

That you vouchsafe your rest here in our Court

Some little time, so by your companies

To draw him on to pleasures, and to gather

So much as from occasion you may glean,

Whether aught to us unknown afflicts him thus

That opened lies within our remedy.


Good gentlemen, he hath much talked of you

And sure I am two men there is not living

To whom he more adheres. If it will please you

To show us so much gentry and good will

As to expend your time with us awhile

For the supply and profit of our hope,

Your visitation shall receive such thanks

As fits a king’s remembrance.


Both your majesties

Might by the sovereign power you have of us

Put your dread pleasures more into command

Than to entreaty.


But we both obey

And here give up ourselves in the full bent

To lay our service freely at your feet

To be commanded.


Thanks, Rosencrantz and gentle Guildenstern.


Thanks, Guildenstern, and gentle Rosencrantz.

And I beseech you instantly to visit

My too much changed son. Go, some of you

And bring these gentlemen where Hamlet is.


Heavens make our presence and our practices

Pleasant and helpful to him.


Ay, amen.

Exeunt Rsncrz, Gldstn, and some Attendants.

Enter Plns.


Th’ ambassadors from Norway, my good lord,

Are joyfully returned.


Thou still hast been the father of good news.


Have I, my lord? I assure my good liege

I hold my duty as I hold my soul,

Both to my God and to my gracious king;

And I do think, or else this brain of mine

Hunts not the trail of policy so sure

As it hath used to do, that I have found

The very cause of Hamlet’s lunacy.


O, speak of that, that do I long to hear.


Give first admittance to th’ ambassadors.

My news shall be the fruit to that great feast.


Thyself do grace to them and bring them in.

Exit Plns.

He tells me, my dear Gertrude, he hath found

The head and source of all your son’s distemper.


I doubt it is no other but the main –

His father’s death and our hasty marriage.


Well, we shall sift him.

Re-enter Plns, with VLTMND and CRNLS.

Welcome, my good friends.

Say, Voltimand, what from our brother Norway?


Most fair return of greetings and desires.

Upon our first he sent out to suppress

His nephew’s levies, which to him appeared

To be a preparation ‘gainst the Polack;

But, better looked into, he truly found

It was against your highness; whereat, grieved

That so his sickness, age and impotence

Was falsely borne in hand, sends out arrests

On Fortinbras, which he, in brief obeys,

Receives rebuke from Norway and, in fine,

Makes vow before his uncle never more

To give th’ assay of arms against your majesty.

Whereon old Norway, overcome with joy,

Gives him threescore thousand crowns in annual fee

And his commission to employ those soldiers

So levied (as before) against the Polack,

With an entreaty herein further shown

Giving a paper.

That it might please you to give quiet pass

Through your dominions for this enterprise

On such regards of safety and allowance

As therein are set down.


It likes us well,

And at our more considered time we’ll read,

Answer and think upon this business;

Meantime, we thank you for your well-took labour.

Go to your rest, at night we’ll feast together:

Most welcome home.

Exeunt Vltmnd and Crnls.


This business is well ended.

My liege, and madam, to expostulate

What majesty should be, what duty is,

Why day is day, night night, and time is time,

Were nothing but to waste night, day and time;

Therefore, brevity is the soul of wit

And tediousness the limbs and outward flourishes.

I will be brief: your noble son is mad.

Mad call I it; for, to define true madness,

What is’t but to be nothing else but mad?

But let that go.


More matter with less art.


Madam, I swear I use no art at all.

That he’s mad, ’tis true: ’tis true ’tis pity;

And pity ’tis ’tis true: a foolish figure!

But farewell it, for I will use no art.

Mad let us grant him then, and now remains

That we find out the cause of this effect –

Or rather say the cause of this defect,

For this effect defective comes by cause.

Thus it remains, and the remainder thus.  Perpend,

I have a daughter — have while she is mine –

Who in her duty and obedience, mark,

Hath given me this. Now gather, and surmise.


To the celestial and my soul’s idol, the most

Beautified Ophelia — That’s an ill phrase, a

Vile phrase, ‘beautified’ is a vile phrase, but

You shall hear – thus in

Her excellent white bosom, these, etc.


Came this from Hamlet to her?


Good madam, stay awhile; I will be faithful.


Doubt thou the stars are fire,

Doubt that the sun doth move,

Doubt truth to be a liar,

But never doubt I love.

O dear Ophelia, I am ill at these numbers. I have not art

to reckon my groans, but that I love thee best, O most best,

believe it. Adieu. Thine evermore, most dear lady, whilst

this machine is to him. Hamlet.

This, in obedience, hath my daughter shown me;

And more about hath his solicitings

As they fell out, by time, by means and place,

All given to mine ear.


But how hath she

Received his love?


What do you think of me?


As of a man faithful and honourable.


I would fain prove so. But what might you think

When I had seen this hot love on the wing –

As I perceived it (I must tell you that)

Before my daughter told me — what might you,

Or my dear majesty your queen here, think

If I had played the desk or table-book,

Or given my heart a working mute and dumb,

Or looked upon this love with idle sight,

What might you think? No, I went round to work

And my young mistress thus I did bespeak:

‘Lord Hamlet is a prince out of thy star.

This must not be.’ and then I prescripts gave her

That she should lock herself from his resort,

Admit no messengers, receive no tokens;

Which done, she took the fruits of my advice,

And he, repelled, a short tale to make,

Fell into a sadness, then into a fast,

Thence to a watch, thence into a weakness

Thence to a lightness, and by this declension

Into the madness wherein now he raves,

And all we mourn for.


Do you think this?


It may be, very like.


Hath there been such a time – I would fain know that –

That I have positively said ‘Tis so

When it proved otherwise?


Not that I know.


Pointing to his head and shoulders

Take this from this if this be otherwise.

If circumstances lead me I will find

Where truth is hid, though it were hid indeed

Within the centre.


How may we try it further?


You know, sometimes he walks four hours together

Here in the lobby?


So he does, indeed.


At such a time I’ll loose my daughter to him.

Be you and I behind an arras then,

Mark the encounter: if he love her not

And be not from his reason fallen thereon

Let me be no assistant for a state,

But keep a farm and carters.


We will try it.


But look where sadly the poor wretch comes reading.


Away, I do beseech you both, away.

I’ll board him presently.  O, give me leave.

Exeunt King, Queen, and Attendants.

Enter HMLT, reading.

How does my good Lord Hamlet?


Well, God-a-mercy.


Do you know me, my lord?


Excellent well, you are a fishmonger.


Not I, my lord.


Then I would you were so honest a man.


Honest, my lord?


Ay, sir, to be honest as this world goes is to be

one man picked out of ten thousand.


That’s very true, my lord.


For if the sun breed maggots in a dead dog,

being a good kissing carrion – have you a daughter?


I have, my lord.


Let her not walk i’ th’ sun: conception is a

blessing but as your daughter may conceive, Friend –

look to’t.



How say you by that? Still harping on

my daughter. Yet he knew me not at first, ‘a said I was a

fishmonger! ‘a is far gone; and truly in my youth I

suffered much extremity for love, very near this.

I’ll speak to him again. What do you read, my lord?


Words, words, words.


What is the matter, my lord?


Between who?


I mean the matter that you read, my lord.


Slanders, sir. For the satirical rogue says here

that old men have grey beards, that their faces are

wrinkled, their eyes purging thick amber and plumtree

gum and that they have a plentiful lack of wit together

with most weak hams – all which, sir, though I most

powerfully and potently believe, yet I hold it not

honesty to have it thus set down. For yourself, sir, shall

grow old as I am – if like a crab you could go




Though this be madness, yet there is

method in’t. – Will you walk out of the air, my lord?


Into my grave.



Indeed, that’s out of the air. How

pregnant sometimes his replies are – a happiness that

often madness hits on, which reason and sanity could

not so prosperously be delivered of. I will leave him and

my daughter. – My lord, I will take my leave of you.


You cannot take from me anything that I will

not more willingly part withal – except my life, except

my life, except my life.


Fare you well, my lord.


These tedious old fools.



You go to seek the Lord Hamlet? there he is.

Rsncrz [To Plns]

God save you, sir!

Exit Plns.


My honoured lord.


My most dear lord.


My excellent good friends. How dost thou,

Guildenstern? Ah, Rosencrantz! Good lads, how do

You both?


As the indifferent children of the earth.


Happy, in that we are not ever happy.

On fortune’s cap we are not the very button.


Nor the soles of her shoe?


Neither, my lord.


Then you live about her waist, or in the middle

of her favours?


‘Faith, her privates we.


In the secret parts of fortune? O, most true –

she is a strumpet. What news?


None, my lord, but the world’s grown



Then is doomsday near – but your news is not

true. But, in the beaten way of friendship, what make

you at Elsinore?


To visit you, my lord, no other occasion.


Beggar that I am, I am ever poor in thanks, but

I thank you, and sure, dear friends, my thanks are too

dear a halfpenny. Were you not sent for? Is it your own

inclining? Is it a free visitation? Come, come, deal justly

with me. come, come. nay speak.


What should we say, my lord?


Anything, but to th’ purpose. You were sent for,

and there is a kind of confession in your looks, which

your modesties have not craft enough to colour. I know

the good king and queen have sent for you.


To what end, my lord?


That you must teach me. But let me conjure

you, by the rights of our fellowship, by the consonancy

of our youth, by the obligation of our ever-preserved

love, and by what more dear a better proposer can

charge you withal, be even and direct with me whether

you were sent for or no.


What say you?


Nay then, I have an eye of you. If you love me,

Hold not off.


My lord, we were sent for.


I will tell you why. so shall my anticipation

prevent your discovery, and your secrecy to the King

and Queen moult no feather. I have of late, but

wherefore I know not, lost all my mirth, forgone all

custom of exercises and, indeed, it goes so heavily with

my disposition that this goodly frame, the earth seems

to me a sterile promontory, this most excellent canopy

the air, look you, this brave o’erhanging firmament,

this majestical roof fretted with golden fire, why it

appeareth nothing to me but a foul and pestilent

congregation of vapours. What a piece of work is a man

– how noble in reason; how infinite in faculties, in form

and moving; how express and admirable in action;

how like an angel in apprehension; how like a god; the

beauty of the world; the paragon of animals. And yet to

me what is this quintessence of dust? Man delights

not me – nor women neither, though by your smiling you

seem to say so.


My lord, there was no such stuff in my



Why did ye laugh then, when I said man

delights not me?


To think, my lord, if you delight not in

Man what lenten entertainment the players shall recieve

from you; we coted them on the way and hither are they

coming to offer you service.


He that plays the King shall be welcome – his

majesty shall have tribute on me – the Adventurous

Knight shall use his foil and target, the lover shall not

sigh gratis, the humorous man shall end his part in

peace, and the lady shall say her mind freely or the

blank verse shall halt for’t. What players are they?


Even those you were wont to take such

delight in, the tragedians of the city.


How chances it they travel? Their residence,

both in reputation and profit, was better both ways.


I think their inhibition comes by the

means of the late innovation.


Do they hold the same estimation they did

when I was in the city? Are they so followed?


No, indeed are they not.


It is not very strange, for my uncle is King of

Denmark, and those that would make mouths at him

while my father lived give twenty, forty, fifty, a hundred

ducats a-piece for his picture in little. ‘Sblood, there is

something in this more than natural if philosophy

could find it out.

Flourish of trumpets within.


There are the players.


Gentlemen, you are welcome to Elsinore. Your

hands, come, then! Th’ appurtenance of welcome is

fashion and ceremony. Let me comply with you in this

garb lest my extent to the players, which I tell you

must show fairly outwards, should more appear like

entertainment than yours. You are welcome. But my

uncle-father and aunt-mother are deceived.


In what, my dear lord?


I am but mad north-north-west. When the

wind is southerly I know a hawk from a handsaw.

Re-enter PLNS.


Well be with you, gentlemen.


Hark you, Guildenstern, and you too – at each

Ear a hearer. That great baby you see there is not yet out

of his swaddling clouts.


Happily he is the second time come to

them, for they say an old man is twice a child.


I will prophesy he comes to tell me of the

Players. Mark it. – You say right, sir, o’ Monday

Morning, ’twas then indeed.


My lord, I have news to tell you.


My lord, I have news to tell you. When Roscius

was an actor in Rome –


The actors are come hither, my lord.


Buzz, buzz.


Upon my honour,


– Then came each actor on his ass.


The best actors in the world, either for

tragedy, comedy, history, pastoral, pastoral-comical,

historical-pastoral, scene individable, or poem

unlimited. Seneca cannot be too heavy nor Plautus too

light for the law of writ and the liberty. These are the

only men.


O Jephthah, judge of Israel, what a treasure hadst



What a treasure had he, my lord?



One fair daughter and no more,

The which he loved passing well.



Still on my daughter.


Am I not i’ th’ right, old Jephthah?


If you call me Jephthah, my lord, I have a

daughter that I love passing well.


Nay, that follows not.


What follows then, my lord?



As by lot,

God wot,

and then, you know,

“It came to pass,

as most like it was.

The first row of the pious chanson will show you more,

for look where my abridgement comes.

Enter four or five Players.

You are welcome, masters, welcome all. I am glad to see

thee well. Welcome, good friends. O old friend, why

thy face is valanced since I saw thee last! Com’st thou to

beard me in Denmark? What, my young lady and

mistress! By’r lady, your ladyship is nearer to heaven

than when I saw you last by the altitude of a chopine.

Pray God your voice, like a piece of uncurrent gold, be

not cracked within the ring. Masters, you are all

welcome. We’ll e’en to’t like French falconers – fly at

anything we see. We’ll have a speech straight. Come,

give us a taste of your quality. Come, a passionate


First Player

What speech, my good lord?


I heard thee speak me a speech once – but it was

never acted,or, if it was, not above once,for the play I

remember pleased not the million, ‘twas caviare to the

general. But it was, as I received it, and others whose

judgements in such matters cried in the top of mine, an

excellent play, well digested in the scenes, set down

with as much modesty as cunning. I remember, one said

there were no sallets in the lines to make the matter

savoury nor no matter in the phrase that might indict

the author of affection, but called it an honest method,

as wholesome as sweet, and by very much more

handsome than fine. One speech in’t I chiefly loved –

‘t was Aeneas’ talk to Dido, and thereabout of it

especially when he speaks of Priam’s slaughter. If it live

in your memory begin at this line – let me see, let me

see –

The rugged Pyrrhus like th’ Hyrcanian beast …

– ‘Tis not so. It begins with Pyrrhus.

The rugged Pyrrhus, he whose sable arms,

Black as his purpose, did the night resemble

When he lay couched in th’ ominous horse,

Hath now this dread and black complexion smeared

With heraldry more dismal, head to foot.

Now is he total gules, horridly tricked

With blood of fathers, mothers, daughters, sons,

Baked and impasted with the parching streets

That lend a tyrannous and a damned light

To their lord’s murder; roasted in wrath and fire,

And thus o’ersized with coagulate gore,

With eyes like carbuncles, the hellish Pyrrhus

Old grandsire Priam seeks.

So, proceed you.


‘Fore God, my lord, well spoken – with good

accent and good discretion.

First Player

Anon he finds him,

Striking too short at Greeks. His antique sword,

Rebellious to his arm, lies where it falls,

Repugnant to command. Unequal matched,

Pyrrhus at Priam drives, in rage strikes wide,

But with the whiff and wind of his fell sword

Th’ unnerved father falls. Then senseless Ilium

Seeming to feel this blow, with flaming top

Stoops to his base, and with a hideous crash

Takes prisoner Pyrrhus’ ear. For lo, his sword

Which was declining on the milky head

Of reverend Priam seemed i’ the air to stick.

So, as a painted tyrant Pyrrhus stood

And like a neutral to his will and matter,

Did nothing.

But as we often see against some storm

A silence in the heavens, the rack stand still,

The bold winds speechless and the orb below

As hush as death, anon the dreadful thunder

Doth rend the region, so after Pyrrhus’ pause

A roused vengeance sets him new a-work

And never did the Cyclops’ hammers fall

On Mars’s armour, forged for proof eterne,

With less remorse than Pyrrhus’ bleeding sword

Now falls on Priam.

Out, out, thou strumpet Fortune! All you gods

In general synod take away her power,

Break all the spokes and fellies from her wheel

And bowl the round nave down the hill of heaven

As low as to the fiends.


This is too long.


It shall to the barber’s, with your beard. Prithee,

say on – he’s for a jig, or a tale of bawdry, or he sleeps

say on, come to Hecuba.

First Player

But who – ah woe – had seen the mobled queen –


‘The mobled queen’!


That’s good.

First Player

– Run barefoot up and down, threatening the flames

With bisson rheum, a clout upon that head

Where late the diadem stood and, for a robe,

About her lank and all – o’erteemed loins,

A blanket in the alarm of fear caught up.

Who this had seen, with tongue in venom steeped,

‘Gainst Fortune’s state would treason have pronounced.

But if the gods themselves did see her then,

When she saw Pyrrhus make malicious sport

In mincing with his sword her husband limbs,

The instant burst of clamour that she made

(Unless things mortal move them not at all)

Would have made milch the burning eyes of heaven

And passion in the gods.


Look where he has not turned his colour and

Has tears in’s eyes. – Prithee, no more!


‘Tis well. I’ll have thee speak out the rest of this

soon. [to Plns] Good my lord, will you see the

players well bestowed? Do you hear, let them be well

used, for they are the abstract and brief chronicles of

the time: after your death you were better have a bad

epitaph than their ill report while you live.


My lord, I will use them according to their



God’s bodkin, man, much better! Use every

Man after his desert, and who shall scape whipping? Use

them after your own honour and dignity – the less they

deserve the more merit is in your bounty. Take them in.


Come, sirs.


Follow him, friends. We’ll hear a play


Exit Plns with all the Players but the First.

Dost thou hear me, old

Friend? Can you play The Murder of Gonzago?

First Player

Ay, my lord.


We’ll ha’t to-morrow night. You could for need,

study a speech of some dozen lines, or sixteen lines,

which I would set down and insert in’t, could you not?

First Player

Ay, my lord.


Very well. Follow that lord – and look you mock

him not.

Exit First Player.

My good friends, I’ll leave

you till night. You are welcome to Elsinore.


Good my lord.


Ay so, God buy to you.

Exeunt Rsncrz and Gldstn.

Now I am alone.

O, what a rogue and peasant slave am I!

Is it not monstrous that this player here,

But in a fiction, in a dream of passion,

Could force his soul so to his own conceit

That from her working all the visage wanned

– Tears in his eyes, distraction in his aspect,

A broken voice, and his whole function suiting

With forms to his conceit – and all for nothing –

For Hecuba?

What’s Hecuba to him, or he to her,

That he should weep for her? What would he do

Had he the motive and that for passion

That I have? He would drown the stage with tears

And cleave the general ear with horrid speech,

Make mad the guilty and appall the free,

Confound the ignorant, and amaze indeed

The very faculties of eyes and ears. Yet I,

A dull and muddy-mettled rascal, peak,

Like John-a-dreams, unpregnant of my cause,

And can say nothing. No, not for a king

Upon whose property and most dear life

A damned defeat was made. Am I a coward?

Who calls me villain, breaks my pate across,

Plucks off my beard, and blows it in my face,

Tweaks me by the nose, gives me the lie i’ th’ throat

As deep as to the lungs? Who does me this,

Ha? ‘Swounds, I should take it. For it cannot be

But I am pigeon-livered and lack gall

To make oppression bitter, or ere this

I should ha’ fatted all the region kites

With this slave’s offal – bloody, bawdy villain,

Remorseless, treacherous, lecherous, kindless villain.

Why, what an ass am I: This is most brave,

That I, the son of a dear murdered,

Prompted to my revenge by heaven and hell,

Must like a whore unpack my heart with words

And fall a-cursing like a very drab,

A stallion! Fie upon’t, foh! About, my brains!

Hum, I have heard

That guilty creatures sitting at a play

Have by the very cunning of the scene

Been struck so to the soul that presently

They have proclaimed their malefactions.

For murder, though it have no tongue, will speak

With most miraculous organ. I’ll have these players

Play something like the murder of my father

Before mine uncle. I’ll observe his looks;

I’ll tent him to the quick. If ‘a do blench

I know my course. The spirit that I have seen

May be a de’il, and the de’il hath power

T’ assume a pleasing shape. Yea, and perhaps

Out of my weakness and my melancholy,

As he is very potent with such spirits,

Abuses me to damn me! I’ll have grounds

More relative than this. The play’s the thing

Wherein I’ll catch the conscience of the King.


New Hopes For New Beginnings!

As sad as I am to leave my previous group members, I was pleasantly surprised at how well my new group meshed together! I have nothing but high hopes for us, as we all seem to have the same ambitions and goals in regards to how we will do with this project. It was no surprise that upon reaching the question in the contract for our anticipated mark, we all said with big smiles, “A+!” I mean, who isn’t aiming for the best possible grade?
Organization seems to come easily to the other girls, which coming from someone who doesn’t naturally have that skill, I am very pleased to say the least. An agenda that we plan on making before each meeting time is sure to keep the ball rolling, and it will make sure we use our time together to the best of our abilities. Procrastination being my middle name, I am thankful to have the necessary pressure of a timeline to keep me focused. Ironically, after I wrote that sentence I focused my attention on “Ellen” for a solid ten minutes. Tsk tsk, will I ever learn?
After rereading Act 4, our designated area of study, I analyzed it more carefully and began to see sort of a pattern within the text. This act is all about anger and harsh tones spoken amongst the characters. Gertrude begins the first scene by explaining the murder of Polonius to Claudius, and how basically there is no hope left for Hamlet. The exasperated feel we get from Gertrude is passed on to Laertes when we see him learn about not only his father’s death but that his only sister has gone completely mad. Exasperation turns to anger, which is followed by the intense need to get revenge on Hamlet for what he has done to him and his family. Claudius participates in Laertes anger by expressing his suspicions against Hamlet who he feels is trying to take the throne from him. A murderous plan is developed between the two characters, and the scene ends with the same amount of anger and anxiety as it did begin with. After seeing the continuing displays of anger, I figured this may be a good start to using my good ol’ faithful (ha!) program Monk to analyze the act more deeply.
Potentially we could use our programs, more specifically we could use Monk, to compare the amount of negative and heated words that are found in act 4 and see if the same feeling is evident amongst other scenes. Although Monk is designated to be used for larger texts, I feel that since we will be comparing this specific act to another act, they will roughly be the same size which should help with producing useful results.

In regards to what my fellow group members may be able to do, I suppose it depends on the specialty that their programs revolve around. Either way I feel like we will be able to get a well rounded amount of results which will help us get right to the bottom of analyzing act 4.

I am slowly going crazy 654321 switch!

I had it easy, but I guess this is where my struggles begin. I don’t think I have hit any level of frustration dealing with these tools until now! I remember back in phase one, my biggest struggles was attempting to figure out how to log in to this blog business and post. Here it begins..

To begin I pasted in the XML file and expected to have some misleading information because Voyeur needs to have characters speaking split from characters names mentioned, as well as stage directions removed. I struggled a bit, attempting to copy all of act 3 into Microsoft word to edit it (LOL). What a mistake that was. I am sorry but 60 pages of editing is not going to happen. What was I thinking?

I know that Tapor has a tool that does this; however, after spending two hours reading phase one blog posts from the team, and also messing around with the Tapor tool, I was unsuccessful in my mission. I was however able to figure out how to separate speakers. Unfortunately I could only get Gertrude’s lines to work and she really only appears in 3.4 (which ALSO keeps including itself in our analysis of 3 to 3.3).

I was also able to figure out how to use the tool from Tapor that counts the caps. I think this is a really unique and useful tool, especially since it is able to pull out names that one would not think to search. For example Jove or God.

Although I learned some great things and not so great things about Tapor, Voyeur is my tool. I am forced at this moment to work with what I have, and what I already know. Until these issues can be ironed out, unfortunately I am using it as it is. I feel like the majority of the tools could be used as a starting point, while Voyeur will be one of the tools used towards the end in order to further our analysis. Therefore, I’ve concluded that my hypothesis to begin analyzing act 3, should be basic, while excluding anything to do with characters specifically (until I can get my issues fixed).

The word cloud! Hamlet is appearing in the biggest font. Thank god. Something is cooperating with me this afternoon.


Working off of the word cloud, love was quite a large word. Love appeared 28 times, while loves and loved appeared once.  This started to make me think about how the word love is used and how it changes throughout act 3 by all characters. This would also be neat to try it with other commonly used words! I found it interesting that “loved” was appearing towards the beginning. The word “loved” is past tense, meaning that Hamlet once did love.  The combination of the words love and loves appear later, but by doing so, it demonstrates a change in feelings.  If Tapor was cooperating with me, I could simply use just Hamlet’s line to analyze how his feelings change from his famous “to be or not to be” speech, to his confrontation with his mother in 3.4. Another tool that is capable of searching for synonyms or even lemmas to determine words similar to love would be useful as well.


When paralleling my tool to other tools, I think Voyeur excels in the ability to do comparisons. I was never one for the frequency charts or graphs, but I can see now how useful these aspects will be for phase 2. There are SO many other avenues that can be explored in act 3, that I didnt know where to begin. Act 3 is where a lot of drama begins and because of that, Ive been super overwhelmed! On top of this feeling of being overwhelmed, my tool has been giving me more problems than ever! Like i mentioned before, until I sit down and find ways to solve my issues, I am kind of at a stand still.

Monk- A Fresh Start


It is a new beginning and I thought a good way to start it off would be to read Act 2 and pick apart some common themes that I found were represented throughout the text. I then thought that I could use the themes that I found to try and see if I could gain more information about them through some analysis tools that Monk has to offer.

While I was reading Act 2 I found that a common theme seen in the text was spying and trying to figure out secrets.  This is seen when Polonius asks Reynaldo to go spy on his son while he is away.  It is also mentioned later in 2.2 when the King and Queen ask Rosencrantz and Guildenstern to spy on Hamlet for them.

After this I decided what information Monk may lend itself to me when associations to the theme spying. I looked up the word concordances and found that there wasn’t any word matches to the word spy or spying in Act 2



I looked back at the language used within the text and I found that the usage of the word spy was not mentioned and a few other synonyms for the word spy weren’t mentioned as well.  I found this to be very odd since when you read the text you know that Reynaldo is sent to go spy on Laertes and Rosencrantz and Guildenstern are sent to go spy on Hamlet, but yet it is not outwardly mentioned. This made me think of the language that Shakespeare himself uses to get across points that we ourselves understand the concepts that differ today.

I then tried to tackle Aprils concept of the Naive Baye’s and look at the language within Act to see if it is compatible to the themes that are easily noted within Act 2.  I looked up at 2 with separation to 2.1 and 2.2 and looked up common words that connect to themes that are seen throughout Hamlet 2.1 and 2.2. to see if the language itself would identify it. I decided to look up “revenge” for Act 2 in a whole,  “spy” in 2.1 and “madness” for 2.2.



As the results show the ideas of revenge by use of the language and words is something that was seen as noticeable in Act 2, but was not very prominent due to the lack of a deeper shade of red. Madness was itself was something that was easily seen within the language due to its darker shade of red. strangely enough the word spy had no language itself noted in 2.1 even though it associates with Polonius asking Reynaldo to spy on his son for him.

I thought it was very odd how some of the common themes that are easily noted within Hamlet are not even noticed or picked up through the language. I may be using the tool wrong or I might not be giving it enough information that I should, but I thought this was very strange.

I think I will find two different aspects of information while working with Monk, one I will find through myself analysis of the text and the other I will find with the analysis through word hoard. Although I wish that some of my personal findings would transfer over to what I find in Monk I think that it brings things on a whole new perspective.

I find that I am going to have to still work closely with my fellow Monk friends just to fully understand concepts and ideas to see if what I am finding may be somewhat correct or if I am going off on something completely wrong. I think it is very helpful to first learn how to use the tool and develop a stronger understanding of it, and with Monk itself you seem to learn more the more you fiddle with it and play with it however it is very tedious.

I hope that I will be helpful to my group, I know Monk isn’t the easiest thing to work with especially on a smaller scale. However I do hope that working with the other tools will help pick up where Monk seems to fall short.

Putting it all together

To begin working with an entirely new group of people and knowing that you are the expert on one tool is slightly daunting.  The responsibilities regarding WordSeer are now entirely on me alone. Scary stuff! Anyways, now that the stress of presenting is over (for now), it is time for me to get back to the text in question: Hamlet, and more specifically Act Two. When I first think back to Act Two, what comes to mind is Scene Two, one of the longest (if not THE longest) scenes within the play. If Act Two had a theme—separate from that of the entire play—it would be apprehension and suspicion. Characters do not just confront each other directly but instead go between other characters, further misinforming all parties involved. Why does no one just ask Hamlet why he is acting strange? Act Two is really the beginning of the rising action of the play: setting up the characters and plotline for the climax of the story. It introduces the players, begins to answer why Hamlet is acting so strange, and creates a conflict between Polonius and his son.

Anyone else remember this from high school english?

Our first group meeting went pretty well, and it allowed me to look more closely—and make connections between—our different tools. The one that I found most intriguing was Voyeur. To have the visuals, word frequency graph, and the play all on one page is very handy. The only part of Voyeur that is somewhat inconvenient is that you have to download the text onto the website. In this example, I used Hamlet’s soliloquy at the end of 2.2. Somehow I managed to add the stop words to the Word Cloud—shocking, I know—and was surprised to find that Hamlet uses Hecuba THREE times in one speech!

Another interesting observation—and one that Prof. Ullyot previously pointed out in a comment—is the similarities between WordSeer and WordHoard. Both focus more on the word frequency and analyzing aspect of a text, rather than the tone and themes of a play. I think this will be a huge advantage for both tools, and will allow us to share information between programs.

Of course it isn’t a normal day in English 203 unless something decides to not work. Today it was GoogleChrome. Now normally this would not be a huge problem for me except that WordSeer works best on GoogleChrome. Sadly, there will be no WordSeer screenshots from me today. Once again, thanks technology!

(I would be extremely grateful to anyone who knows how to make GoogleChrome a permitted program in my firewall settings!)

In regards to Act Two specifically, I think our group has a lot to get through. Hamlet has multiple soliloquies throughout this act, Rosencrantz and Guildenstern make an appearance, and everyone is trying to figure out what is wrong with Hamlet. Analyzing this act calls for multiple read-throughs and discussions, as well as collaborations between tools and what each does to answer our questions.

Oy, we kan/ddo rreap moorre oov WS trru DH!

“Oy, we kan/ddo rreap moorre oov WS trru DH!” Do you get it? If you did you can officially call yourself a “Scrabble Freak”…smarty pants. If you did not get it, you can call yourself, umm, normal? 

A little bit of an explanation:


 But why would I spend the time (I won’t tell you how much…) putting these letters together? To show how all five tools can cohesively come together…the same way our new groups are coming together for Phase 2 of our projects.  The fact that some groups faced difficulty in the previous phase is actually particularly convenient for me here. Their difficulties are represented by my interesting and “difficult” spelling choices. Obviously I did this on purpose…

In all honesty, I think that it is at this point that some truly interesting and useful discoveries are going to be made within Hamlet.  In Phase one, everyone was trying to figure out their tool and become the “expert” of it. In Phase two, however, it seems we as teams will be dealing a lot more with the text itself (specifically our designated Acts). 

 As a member of the “Act 1” group I am feeling interested but sceptical.  What could our group possibly uncover that could compare to the “Act 3” or “Act 4” group.  We do have a ghost cameo, which is pretty cool, but lets be honest – we all want to rip apart Hamlet’s famous “To be, or not to be” speech.  To dissect that speech with even one tool, Wordseer for example, could prove to be tremendously insightful. I am certainly interested to see what that group comes up with!

 That being said, maybe Act 1 will come out as the Underdog in this project. I am, admittedly, apprehensive of the results we may find in this act; however, perhaps my own lack of interest will spark a higher level of interest for myself: a challenge.  Will being assigned a comparatively less interesting act push me to search for the unobvious?   A Shakespearean “Where’s Waldo”. COOL.


Coming out on the other side of this blog post I am feeling a little more excited about Act 1. What can we, bringing all five tools together as a team, really discover about this act? How much more “kan” we “rreap oov WS truu DH”? Is Waldo hiding in the pages of Hamlet’s Act 1? It’s all about his trademark stripped shirt: obnoxious and begging to be noticed, but also ridiculously easy to overlook.  You can’t see it…until you do.

Death, Death, Death- Or is that it? (Phase Two, Blog Post One)

Throughout the course of this post, it is my intention to explore the relationship between my interpretations of the text of Hamlet acquired
through traditional text analysis and my closed reading of the text, and the interpretations drawn from employing my tool of expertise, word seer, to critically analyzing certain fragments, in this case, a single act of the play. I will then highlight how these two approaches compare to one another, and pose further questions as to how this comparison may be capitalized upon in order to generate new conclusions or insightful observations.  I would first like to express that I have always been sceptical of classifying Hamlet as a conventional tragedy, as the protagonist Hamlet deviates from the characteristic traits of the tragic hero, and commits no apparent “mistaken act”, and the majority of action does not culminate until the bloodbath of act five, coincidentally, the act I have been assigned to study. Therefore, my interpretation of this act can largely be characterized by the observation that it alone defines this iconic play as the tragedy it has come to be widely recognized as. Without summarizing the plot of the text, it is evident that the catastrophe and other defining aspects of Aristotle’s conceptions of the genre
of tragedy are reserved almost exclusively for act five, as Hamlet and a series of characters surrounding him, including the villainous king Claudius who he seeks to exact a vendetta upon, face their untimely demise. So what does this mean to my interpretation? Death, death, death. Futility, futility, futility. Basically, I believe that the bard is trying to express to us a message that revenge only manifests as death, and highlights the futility of life the struggle associated with it. My interpretation is one among many, however, of course. Even as superficial a source as Wikipedia recognizes a multitude of proposed and perceived contexts and themes that the text carries.

Now, this interpretation is just fine, on a superficial, redundant, basis. However, when we seek to uncover new insights and avenues for exploration that have rarely been embarked on, it is also effective to employ tools such as word seer to aid in identifying new trends.

So, how would I gauge word seer’s interpretation of act five of Hamlet, and, in turn, compare it with my own? In considering the advantages of word seer that my group outlined during phase one of our team assignments, I felt obliged to plug in some words, pertaining to act five in particular, that I felt could be conducive to word seer’s processing. Simple enough, right? However, this was not the case; incidentally, I was unable to segregate the fifth act of Hamlet alone(a task I will fixate on more as my research progresses), therefore, I felt it fitting to exhaust the next best alternative, in examining the entire text again using word seer, in order to apply it more broadly to my interpretations of act five, alone. I must comment, of course, that this may be an instance in which another tool, such as voyeur and its image qualities, could well supplement word seer’s shortcomings. Regardless, I decided to employ words that I feel characterize the themes of Hamlet, and proceeded to observe the concentration of them throughout the text, paying particular attention to the words that more frequently occur towards the end of the text, that being, act five. In this case, I searched “death”(as a fundamental), “life”, “duty”, and “soul”, in order to observe whether or not they appeared heavily towards the text. (These results are pictured below). However, what I was surprised to find was that these words, which all carry emphasis within the “revenge” tragedy, were dispersed throughout the entire text, and did not exceptionally exceed their counterparts near the end of the play.

What exactly did I make of this? Ironically, this interpretation provided by word seer, identifying that none of these words completely define the text in frequency, contrasts to my own closed reading and textual analysis interpretations in demonstrating that words themselves do not necessarily develop into a coherent indicator of theme. Therefore, I am intrigued towards studying speech patterns and speaker frequencies in order to expand my perspective regarding interpreting Hamlet, a process which, I will conclude, could be better achieved by other tools.

Before giving up on my previously advocated frequent words constituting theme theory, I intend, one last time, to compare “death” and “honour” with the other texts in the Shakespeare corpus, in order to see if the frequency exceeds the other texts, perhaps suggesting that Hamlet is founded more on characters, speeches, and themes that favour these words. For now, however, I will reiterate the relationship, and comparison, between my interpretations of Hamlet, and those suggested by my findings in word seer.

It would be an exaggeration to suggest that the two interpretations I worked with were at dueling odds with one another. While word seer’s findings didn’t exactly support my interpretations, (the results would have verified my interpretations were they to contain a higher concentration of the inputted words near the end of the play) they certainly aided me in recognizing that interpretations and perspectives should not be taken at face value, in that, words that are suspected to occur frequently do not constitute the theme of text. Therefore, while my interpretations are geared more towards my closed reading, the word seer interpretations helped me to be aware of leaning towards
one theory or conclusion without considering varying alternatives, and this is the ultimate underlying relationship between what I found and suspected, and what word seer supplemented it with.  In my next post, I will further explore this relationship, in using more detailed
approaches with more specific functions of word seer, and perhaps even other tools that my new group members specialize in, in narrowing down my search more successfully to act five alone. However, this being said, no one is a complete expert, and one of the fundamental values in research is to adapt and learn as one progresses, and that is what I intend to do.

The End of Things: WordHoard Presentation

For our group analysis of Act 3.4 we decided the best way to go about was to pick an overall subject in the scene and then divide that into specific questions. Each group member was assigned one question that she was responsible for analyzing. The end result would be the group members combining their results to give an overall analysis of our main subject; the relationship between Hamlet and Gertrude. Phase One gave us great insight into our tool as well as the other tools. Can’t wait to use multiple tools in Phase Two!

Click here WordHoard to view the PowerPoint we used during our presentation. Cheers!

WordHoard: Meant for Something Bigger

When I first started using WordHoard, I was excited. Who wouldn’t be when it came to using a program with so many possibilities? As I mentioned in my previous blog, WordHoard has numerous functions which break down even further to other functions which give very specific results for analyses. This concept of subdividing from a major function was implemented into our group analysis.

Our group took the broad question: what is Hamlet’s relationship with Gertrude and came up with more specific questions which each group member would analysis using WordHoard. I took on the task of analyzing the question; does Hamlet blame Gertrude for the murder of his father? My initial plan was to search Hamlets speech for words and phrases which show resentment towards Gertrude and phrases where Hamlet tries to make Gertrude feel guilty for what had transpired between the king and Claudius. This line of thought was not easy to analyze.

Before I list my endless problems with WordHoard, I will begin by explaining what the main purpose of WordHoard is; the collection of words. WordHoard is great for someone who is searching for the amount of times Hamlet says love or the number of times Ophelia uses the term madness. This is great for someone who is analyzing different plays of Shakespeare and comparing the results of the two, but it doesn’t compare just the one act or scene from the play; this my friends, is one of my major limitations.

While doing my research I tried unsuccessfully to analyze Hamlets speech in 3.4; this was unsuccessful because WordHoard either (1) takes the reference play and compares it to another Shakespeare book or (2) compares the wording throughout the one play. I believe that our program would be great if we took Hamlet and compared it to Romeo and Juliet or Othello. Trying to compare the tone change within the one scene is unfortunately unavailable.

Going back to my research, I decided to see how many times Hamlet actually uses the term mother when referring to Gertrude; the result wasn’t very insightful for my purposes. Instead of the information I was looking for I got the following data for the historical occurrence of mother in all of Shakespeare’s tragedies.

WordHoard is a great program for people who want to search up a specific word and compare it between two separate texts. It will easily show which words are nouns and verbs or when they were first used; unluckily it will not explain why the character uses the word or the tone in which he delivers his lines. In order to start my analysis I had to look up the lines I wanted to study from my text and then search them up on WordHoard.

WordHoard is still, at least in my view, a great program which should be used for broader research. In phase 2 I believe our program will be more effective when we must analysis the entire text.



Seeing Eye to Eye

After a bumpy road of fiddling, frustrations, and findings – I believe I have broken through the surface of being worthy of the title “Voyant,” or perhaps as the french may call it: “voyeuse.”   Cheap jokes aside, I feel I have molded my mind enough around  Voyeur to be able to call it my specialized field above others.  Although I initially lacked this confident resolve in my previous post, continued meetings with a constructive team has seen me through to viewing Voyeur and Hamlet with a fresh set of eyes.
The tool enables a broad look at word connectivity within the text. Visual tools like “knots,” “word trends” (as examined in “Getting Off on a Bad Foot”) and “lava” provide a variety of mediums through which to display evidence in a specific fashion or equally varied to appeal to a broader user base. For each and ever self-contained “side tool” there is the option to either play or to read further into it so previous knowledge of any tool is completely unnecessary.   Our group met with more than a little confusion when attempting to analyze the mystery surrounding  knot interpretation.  After both playing with it individually and within group meetings we have come to a semi-understanding of the somewhat erratic knotting patterns.  Without the Hermeneuti information page, we would not have had any clue where to start in comprehending the tangling mess.  Any  way you choose to slice it, Voyeur is undoubtedly user friendly and that is potentially the key to what sets this apart from both its predecessor TAPor and as well from all other digital analysis aids.

As far as analyzing 3.4 has gone, the only obstacle I have encountered has been my own stubborn preference.  As a group, we have come up with several ways in which to tackle interpretation using our tool.  No matter which hypothesis we might have selected, we would have an ample amount of evidence supporting it due to our new understanding of Voyeur.
Some examples have been*:

  • Is Hamlet truly feigning madness or is it deeper than he fully understands?
  • Sexual tensions and the relationship between Gertrude and Hamlet – strictly familial?
  • What is the purpose of Polonius in this scene and why did his death come about in such an under-exaggerated manner?
  • Analysis of the presence of the ghost and the only tender picture painted for Hamlet and his dysfunctional family.

*Stay tuned to find out where we went with our brilliance…Coming to you, this Monday at 9AM (MT)!
On my own, I have played around with both aspects of scene versus entire corpus analysis and I far prefer examining the entire play and other plays/literary works in conjunction with  Hamlet. Although Voyeur has proven more than useful and enlightening to examine a specific scene and its advantages are obvious – my specific tastes lead me to seek wider horizons.  Perhaps my eyes are bigger than my stomach, however phase one has but whet my appetite for the main course next phase.
On another note, one of phase 1’s project requirements realized with the highest has been having been part  of such a reliable and hard working group.  There has been plenty I, and each one of us for that  matter, have put forward individually.  However, it would have taken a considerably longer time if we had not all pushed forward in united effort.  For every discovery that I have personally made using Voyeur, such as seeking out connections of good and evil and their escalating value within the play, I have had at least one peer add their discoveries to my own creating more concrete conclusions rather than theories. Past academic experience has proven a particular rarity in being placed in a group of such high work ethic and dependability.  Our communication is solid both inside and out of meetings and peer brainstorming is equally distributed and all opinions examined with respect.  Aside from newly acquired expertise, I would certainly  bring the copacetic nature that this group has exhibited forward into phase two.

MONK: Truly, “more matter with less art!”

The last time I wrote, the content of my post focused on frustrations that I had experienced with the limitations on the capabilities of MONK, and the difficulty I experienced even approaching my starting question of what our tool could do to provide us with insight about Hamlet 3.4, that we couldn’t get from just reading the text. Needless to say, the content of this post is very different.

For the duration of our team meeting today, we prepared to deliver our presentation on MONK and its capabilities and explain how it led us to new understandings of Hamlet 3.4. When dividing the topics to be discussed, I found myself assigned with the task of explaining the classification methods that MONK uses, Naive Bayes and decision tree induction, and how MONK uses them to provide useful knowledge. These, being concepts that I had a grasp of (a slippery grasp at that), I felt comfortable in explaining to my fellow team members the information I had absorbed from reading the night before.

Well, as I began talking and explaining my findings by referring to the actual process of using the methods, I realized I hardly understood exactly what I was talking about or where my vague and unconfident sentences were taking me. It was after that meeting that I sat down and furiously (or with committed fervour rather), researched, practiced, and practiced again until I understood exactly how these were to be helpful to our analysis. The following is what I found.

Text mining or also called data mining, in its shortest possible form of explanation, is a process that revolves around pure mathematical data analytics in order to return statistical data and probabilities based on patterns and sequences observed in the data. MONK, using Naive Bayes and decision tree induction, is among these text mining methods.

The tutorials for Naive Bayes and decision tree induction provide detailed, technical explanations of what they are and the processes of these analytics. In my attempt to get a better understanding of these analytics, I started with these tutorials. For those of you who read them, you will see that when I say detailed and technical, I mean that it looks like english but there were moments when I doubted that it really was.

This section (below), is only half english.

This one, is most definitely not english.

So, I turned where all students turn for short and quick explanations: Wikipedia. In my brief descriptions to follow, there are terms that I must first address in order for the explanations to be coherent.

  • Training sets– sets of data used to discover parameters that can provide a probability of predictable relationships between two or more sets of data.
  • Test set– A set of data used to asses the strength of the probability that was given by the training sets.
  • Over fitting– Crucial to training sets, are when statistical models (such as those in MONK) emphasize and display the minor fluctuations and random errors in the data instead of the relevant relationship, because there are more parameters than there are potential observations.
Naive Bayes is a classification method that uses two or more “classes” that are assigned to training sets. It builds knowledge and “learns” comparisons between the two classes, and applies them to classify an unknown text. It is useful for 3 things:
  1. Categorizing a text.
  2. Finding features that stand out in a text.
  3. Characteristics of one text that are common to a large body of texts, like a genre.
The MONK tutorial points out that the interesting aspects that can be seen using Naive Bayes, are those that we would consider “misclassifications.” In this way, Naive Bayes is useful for making a hypothesis and testing it, or going through the process to confirm something you believe you already know.
Decision tree inductions take the classifications provided by Naive Bayes, and use them to determine the attributes or characteristics that made them so. Below, is a simplified and understandable image of the basic concept of a decision tree, provided by the MONK tutorial.
This is the process that is applied to the data analytics of the decision tree. It determines which aspects are present and which are not, and then logically produces a ‘tree’ of information that leads to probabilities.
This is where over fitting is a crucial aspect. When this models grows to become too complex, this means the training data will be too detailed, therefore essentially useless in analyzing texts other than the training set. Instead of ‘learning’ the general relationship between the ideas, it memorizes that particular training set and attempts to apply it elsewhere.
The purpose of my explaining the analytics behind the tool, is because once I understood what the tool was searching for, and how it searched, it made it far easier for me to understand how to use the tool. With a body of text, and a tool that compares one body of texts, to one or more other bodies of texts, it is extremely difficult to determine what to look for that could be significant. Being given the probability and frequencies of words in texts is, despite how simple it may sound, a difficult place to start because there are just too many words.
Nevertheless, this is what I learned.
In general, using the classification tools that MONK had to offer, and practicing using them correctly did not further my understanding of Hamlet 3.4 as much as I had hoped, however, it did confirm what I believed, surprise me with things I believed that were wrong, and open for me a door into the digital humanities by showing me its vast capabilities. For example:
In terms of Hamlet 3.4, I attempted to analyze the scene in comparison to the all the tragedies in order to find what of this scene was characteristically tragic in Shakespeare’s language. Unfortunately, the way that worksets are defined, the closest I could get to this kind of analysis was Hamlet compared to all Shakespeare’s tragedies, and 3.4 compared to the remainder of Act 3. There I became faced with a problem also, what parameters do i assign each scene in order to find out something useful about 3.4?
In the section where it says “click to rate” there is a certain parameter that you are setting. If you filled in “love,” “death, and “betrayal” as themes of the first three scenes into the first three spaces, and hit ‘continue’ then it would return to you the conclusion of which theme scene 4 best fit according to the probability determined by Naive Bayes. Doing this, unfortunately returned no substantial results as the interactions within the individual scenes themselves were too varied from scene to scene.
In attempting to compare the nature of Hamlet to the tragedies, I did the following:
After hitting continue, I set the following parameters:
These parameters returned to me the following classifications using Naive Bayes algorithm:
The intensity of the red next to the title of the play indicates the level of confidence, or the lowest probability of error, that its classification is correct. The predicted rating, is the classification that Naive Bayes provides, based on the 2 classes (historical and fictional) that I have set for it.  From this, Naive Bayes shows me that it is fairly certain that based on the data I have provided and the data that it has analyzed, there is a certain % probability that it is a fictional play.
When i click Hamlet and the continue, MONK shows me the data that it has found which explains its confidence level.
The nouns that appear in the far right column are those that have given the Naive Bayes algorithm reason for the presence of probable confidence. The “Avg. Freq. Training” column is the number of times that the word appears in the ‘parameter’ plays that I labelled before, and the “Avg Freq Test” column is the number of times that the word appears in the plays that I left to be classified.
The reason that the confidence is not vibrant red in the predictions however, is because of the infrequent words that appear below:
When I click “Decision Tree,” the image that pops up displays the process by which the analytics flipped the tree over to determine what word could act as a classification.
The results displayed above provides the probability of error of the word “unkindness” as the basis of that classification. This decision tree states that in terms of probability, this word had the lowest error rate, and highest predictive performance.
Therefore, from this data, I can conclude that Naive Bayes and the decision tree have determined that there is a higher probability that Hamlet is a play of fiction, rather than history.
In conclusion, despite the various frustrations the group has experienced and the little bits that we picked up about 3.4 in specific, through Naive Bayes and decision tree induction, I have learned that classifications are a great place to start. Comparing texts in order to determine aspects of one based off another CAN show you something you never knew, or prove you wrong, in order to provide you with some idea of what you need to look for or what research criteria you need to change.
In terms of research, as we’re doing in ENGL203, learning and being wrong…I think that’s a great way to start.




Wordseer: The Problems and the Possibilities

So I was at my group meeting on Friday, and, wouldn’t you guess, our tool, Wordseer, wasn’t up. That’s to be expected occaisonally with any program you find hosted on the internet, because servers crash, updates are installed, tested, etc. but then it happened again today.



When, over the course of a week, the tool is down twice at the very least. It starts to indicate, at least to me, that it has some technical issues to solve. Now, I’m a computer-science major as well as an English one, so I understand technical difficulties, and accept that there are plenty of tools out there with such difficulties… But not all their problems are technical.


For the purpose and remainder of this blog, I’m going to assume a hypothetical next genereation Wordseer and to this Wordseer 2.0 I’m going to attribute as many things that would be helpful as possible. This way I would be suggesting improvments as opposed to criticizing Wordseer for what it is not.


The first and most useful thing that is missing and could be included in a new iteration is a text uploader. This way you could analyze any text that you want. Currently the selection is a) written by Shakespeare, b) written by Stephen Crane, or c) related to slaves. Doing this would give users a far broader volume of text, but also would allow someone to take a text and easily use a tool like Tapor to extract pieces of text, for a more versatile analysis. For example, Dr. Ullyot wanted us to try and find a way to analyse Hamlet 3.4, but lacking any function to do so, our group was incapable of analysing any one portion of Hamlet. If we could upload an xml, text, or html file to be read, we could then upload just 3.4 and analyse the document. With this theoretical addition, one could also upload just one speech, or the lines of one character, or a section that the user has found that is written in a certain meter. Any of these and just about any other selection of text would help a user find more specific, varying, and interesting results.



Another function that could be included would be to report bugs in the software searching for relations, because these do, occaisonally, pop up. This would help the creator of the software to better understand and develop the tool to become more accurate over time. These things happen, it’s easier to report a bug if you just press a button pertaining to one search result that turned up when it doesn’t apply. This would help the creator of the software to help the users of the software to have more varied and more appropriate search results and making his or her experience simpler and more effective.


The last addition I think could be added is the possibility of private and public functions which would apply to such current functions as tags, annotations, and collections. Things not already included that could have both private and public attributes could be saved search results, documents that the user uploaded (as per earlier in this same blog) or even forums or chat. This would enable collaborative work through a) the entirety of the digital Humanities field b) a small group of students or researchers working on a research paper or project or c) just the one user. It would enable the users in the neccessary groups to have access to everything they need or want and eliminate the unneccessary annotations and documents.


There are currently 3 ENGL203 and 4 Hamlet related documents, all of them public.


Now, I realize that this is largely the criticisms, of a computer-science student, but it is also the opinion of a Wordseer user and English student. I think Wordseer has potential as a fun and intensely useful tool that could help students come up with theses for their papers, but right now it is limited to, well, let’s be honest, no one’s going to search the relationships between words in works about slaves, and not too many digital humanists will be interested in Stephen Crane’s works; right now it’s limited to Shakespeare and limited within it by subdividing walls at that.

WordSeer’s Clean comeback- Finally Satisfied!

Second blog post on WordSeer…let’s see, there was definitely many ups and downs!

First off, I think it is super sweet that Aditi Muralidharan took the time to read my blog and apologize for the slow speed of the program. Aditi, it isn’t your fault, this is how everything in life gets better- with trial and error- but I still greatly appreciate your sincere concern (<3). I came to realize this when working in my group on our presentation that we plan to show the class on Friday. I have a super great group and with all of our ideas and feedback it was going through the same process of getting through problems and finding the best way to convey our findings in the most correct and accurate way as possible. PS- spending 20 minutes on a sentence was well worth it! We discovered new things such as the “newspaper” application (thank you Richelle for this!) that highlights one word throughout the entire text. This was a really great finding, however, we as a group ran into the problem of not being able to do a “snippet” (highlighting an entire scene of the play) therefore not enabling us to use the newspaper application as we couldn’t get the one scene of 3.4 alone. This made it difficult to compare as we only knew the findings for the entire play leading us to the issue of not knowing how to include this in our presentation.

The neat thing in general about WordSeer is that through the “read and annotate” button (which at first many of us struggled with) we were able to make notes and highlights. We realized that even though it was a challenge to get just 3.4., when studying the entire text it is an amazing tool once the browser glitches are fixed. It is wonderful in doing initial research, allowing one to get a hypothesis and a good start in using other tools and methods to continue the research. The program is really organized, and lets us put our notes and tags directly on the text which is readily available too which serves as another convenience! WordSeer, I believe, is the best tool in finding certain words in a text and providing evidence that shows the significance of those words which leads to the understanding of theme and tone, characters, and meaning.

The one thing that I cannot be happier about in concerns to my digital humanities program is that WordSeer has a simple face, easing the troubled minds of the technologically challenged such as myself. Sure, I ran into problems, but in the overall experience of using this program- it came to be more pleasant than frustrating. It also helped that my group and I were very open with each other and we worked as a great team! Thanks guys, you all rock!

Once we navigated through the initial obstacles, the more we got used to our program the easier and less complicated it got. The results were clean and polished, and provided us many answers, even more than we had expected. It shows qualitative and quantitative answers, each result leading us to more search options and ideas.

To be honest, since WordSeer seemed the easiest program to use- I admit that I underestimated its capabilities. I remember in one of my twitter questions, I had asked if WordSeer would provide vague and simple answers as the program appeared basic compared to the more techie tools such as Tapor. I was pleasantly surprised to know that complicated and hard doesn’t lead to more intelligent and detailed results. Less is more in this case, and what a relief I tell you!


Moving Forward With WordHoard

Blogging, in my mind, has always been an activity that is done individually.  It is a way to express one’s thoughts and opinions to the world and in turn allows people to respond.  This is why taking on blogging assignments as a team is a tricky and new experience for all of us!  I have to be thinking about how my individual blog post can contribute to the overall findings of the group. The subjects I choose to talk about in my blog posts should be interconnected with my 4 other group members’ posts in order to create some consistency in the team’s results.  In order to achieve this, my team decided to focus our analysis on one main subject.  The subject we chose to analyze in Hamlet 3.4 was the nature of the relationship between Hamlet and Gertrude.  We each picked an aspect of 3.4 that could potentially tell us something new about the mother/son relationship of Hamlet and Gertrude, and are attempting to use WordHoard to help us obtain new understanding on this subject.  Heavy on the word attempt.  Once we have all analyzed our individual parts, we plan on bringing our findings back together and smoothing it out into one cohesive idea.

The aspect of Hamlet and Gertrude’s relationship that I am using WordHoard to help me analyze is whether or not Gertrude really believed Hamlet was mad.  How did Gertrude react to Hamlet when he began speaking to the ghost?  Did Gertrude know the ghost was there, or did she really believe her son is crazy?

WordHoard, in all honesty, doesn’t do a whole lot in comparison to some of the other tools.  Its main function is to look up word frequency and shows you when the words and their lemmas are used.  Since my initial experience with WordHoard, I have made an intentional effort to be more careful with my word searches.  I talked a lot in my last blog post about how irritating it was that every time I wanted to change a little part of my search, I had to start over from scratch.  By being more careful and specific with my queries this time around, I’ve managed to save a bit of time while submitting information.

The relationship between Hamlet and Gertrude in 3.4 is very complex.  But because Polonius was only present for the first few lines of the scene, it made it easier to make inferences from my WordHoard searches because I knew most of the results would be from conversation between Hamlet and his mother (with the exception of the Ghost’s lines) and so I didn’t have to be as specific with my search.

My first instinct was to search for the word “mad”. This yielded one result in 3.4, a line in which Gertrude blatantly states “Alas, he’s mad.” as soon as the Ghost enters and Hamlet begins speaking to him.

This line alone makes it fairly clear that Gertrude believes her son is crazy.  WordHoard found the word, gave me the exact line from which it came and also gave me the context.  What I do wish WordHoard could do that other tools can is to search for synonyms.  By searching a word such as mad, I also could have found places that Gertrude continues to question her son’s sanity.  For example, when she tells Hamlet “upon the heat and flame of thy distemper sprinkle cool patience!”, distemper means to have an unbalanced mind.  This would be a synonym for mad.

Another thing I decided to search was the ratio of words Hamlet says compared to Gertrude.  WordHoard doesn’t require you to enter an actual word to incur results, instead you can simply ask it how many words a certain speaker says in a certain scene and it can retrieve a number for you (however it can take a long time loading).


Gertrude spoke 303 words in 3.4, and Hamlet spoke 1343 words.

In my own personal opinion, if I were having a conversation with someone whom I believed to be crazy, I wouldn’t be saying very much either.  This extreme ratio that WordHoard have given us could argue that in Gertrude’s eyes, Hamlet was rambling on and on, talking to “ghosts”, and just not making a whole lot of sense so she stayed fairly quiet.

I know there are several theories floating around saying that maybe Gertrude just didn’t want to see the Ghost, and maybe Hamlet wasn’t actually crazy.  But from what I can see from the few examples that WordHoard has given me, I do think Gertrude feared the sanity of her son.  WordHoard paired with some other tools could really allow this theory to be analyzed deeper, and I look forward to being able to do such things when we get to Phase 2 in the semester.

What Could Be Better Than This?

As I mentioned in my previous blog post, I am not very technologically savvy, and because of that, I was very unsure about the use of technology in literary analysis.  For the first phase of our group projects, I managed to make what could be considered a terrible blunder by not attending the tutorial for voyeur, which happened to be the tool I am working with now.  Over the course of the last few weeks, I have discovered that Voyeur is an extremely user friendly tool that is very adaptable to the person using it.  I have discovered all that I have about voyeur with the help of my teammates, and simply by playing with the program and testing its boundaries.  The most common issue with voyeur amongst the people working on it is the redundancy of the tools in it.  Yes you have things like the word cloud called Cirrus and the bubble words which are basically the same thing except that Cirrus is much more visually appealing.  There are other similar examples of tools in Voyeur that are fundamentally similar, yet different, like the word knot and the word frequency chart, yes the Knot is more appealing visually, but the frequency chart is just easier to read for me personally.  The examples that I have just given may seem redundant, but they really are not, they are basically the same tool that has been altered so that it applies to different people and their respectively different ways of acquiring information.  However, I must agree with all of my colleagues regarding things such as the Lava tool or the Term Fountain.  I believe the sentiment was made in one of the comments on Ruby’s post that they look kind of like a piece of impressionist art.  It really is a valid idea that the makers of voyeur have attempted to put so much effort into visual learning that they have strayed beyond visual learning and into the field of art with something like this:

It actually makes little sense to me; there is no explanation of what the little bouncing dots mean, there is also no way to input parameters apparent to me unless you were to input a very precise file into the search box at the very beginning.  All in all, Voyeur is easily my favorite of all the programs with its simple interface right when you begin to use it; it has a very wide open site that is welcoming and pleasant right when you start.

I would have to agree with Dr. Ullyot’s sentiment that is kind of like Google, a simple search and go site right at the beginning.

From this point onwards, all a person has to do is enter a file in for them to search, and you instantly have several tools to analyze it right at your fingertips. What could be better than that?

TAPoR vs Teresa: Round 2

From my first foray into TAPoR, I was left feeling extremely discouraged and very frustrated. Being the only group member without yielding any results besides, finding a few capital letters, made me feel a bit heartbroken. I do, however, have to announce that this second go with TAPoR has been showing me positive results. I still cannot say, that TAPoR and I are on great terms but we have definitely made progress in our relationship. Instead of only getting error messages 100% of the time, I now get them about 50%. I mark that as a huge step in the right direction.

The very aesthetically pleasing word cloud and I have become fast friends. It always works without error and I can manipulate it by taking out words like: “the”, “and” and “but”. The word cloud tool does exactly what it sounds. It makes a pretty colorful cloud with different sized words. The words that are most commonly used in the text are bigger then the words who were not used. This tool is definitely a tool to use as a jumping off spot at the beginning of your research. I was able to pull important words from this and then using the same words to look at a deeper meaning within the text.


This, unfortunately, is where my progress stopped since the other tools still refuse to succumb to my persistence. As I was working through my problems with the program, I have found some interesting facts of the good parts of the program and its limitations. The most important piece of information I have gathered is that TAPoR is a program that you need to use more then one of its many tools. By just looking at a word cloud, the research and information you gather is unfortunately useless, unless you utilize the other tools. Concordance, and collocates are tools (if they would cooperate) I would use next to continue on my research path. I have yet to get them to cooperate but I am sure that they will cave eventually.

From working within my group another piece of information I have gathered. That is, no matter who, when and how, it is hard to get the same results as your team member. Matt and I noticed that we had different findings using the caps finder. Even though we both used the same version of Hamlet (the URL from the Hamlet blog) we ended up having different results. Being curious, we decided to do a group caps finder. We used the same tool, at the same time, with the same URL, to see what results we will gather. Not only did our computers lag but, only 2 out of 5 group members gathered the same results. Thus being said, I only found this a small frustration then my initial run of the program last week.

Besides the caps finder giving us different results, I find TAPoR starting to work well with digitally analyzing text for us, beginner digital humanist. This as a good method to quickly pull out quantitative results without having to slave over an act; saving us time without the need of our trusty highlighter, highlighting the all common themes and words. Who knew a computer program can do that for you in a blink of an eye? However, this tool, to me, is still only a supplement that can help your research NOT the substitute to the actual text.

For now, I believe I am on the right track and I am finding some results. Questions are definitely being answered and work is moving in the right direction. Feeling more confident then before, I see the light at the end of a very long dark tunnel.

Some comedic relief for the computer discouraged, here are some error messages I have received since my last post. Enjoy 🙂

Art Deco and Flexibility

While Voyeur possesses some wonderful tools for comprehending text, there are also some drawbacks, (although to be fair all of the tools have both their positives and negatives). Referring to my teammate Ruby’s post, ( I completely agree with her about strange and redundant tools within Voyeur. The Knots tool was one tool that I specifically remember from the tutorial and I remember wanting to look more into it, however, I find using this tool provides little to no assistance in understanding a text. It is a tool for very visual learners and looks at the ‘path’ of words throughout the corpus and where they intersect with other words.

When you click on each “section”  of the knots, it takes you to where that word exists in the corpus. I find this tool to be messy and while it may be helpful for some people, my group and I agree that the Word Trends tool does the same job with more accuracy and less confusion. Other tools offered by Voyeur share the same issue, looking more like art deco then a comprehensive tool. Some tools in Voyeur accomplish the same task in different ways. Take for example the Bubble tool and the Word Cloud tool.


Word Cloud:

Both tools express the most frequently used words and organize them into a visual representation of that frequency. Now the main differences between the two tools are that in the Bubbles tool, there is a list of the top fifty, most frequent words in the corpus beside the visual and in Word Cloud the words are not separated and are expressed in different colours. One other difference is when you mouse over the words in Word Cloud you are shown how many times the word is used whereas in Bubbles that function is unavailable. Other then those minor differences, I find no real difference in function between the two tools. I personally prefer Word Cloud, however I would not mind some feedback as to why Voyeur developed what I find to be two extremely similar tools that accomplish the same purpose rather then developing a different tool that examines text in a different way.

Now it is not all bad. The Voyeurans and I have discovered many different uses for the tools that we do enjoy using and have discovered Voyeur to be a very flexible program which not only answers questions, but prompts new ones. I enjoy the freedom to examine different parts of the text on their own rather then being forced to examine the entire corpus/act/scene etc. For example, I can isolate the part of the scene when the Ghost is present and examine it separately from the other parts of the text where the Ghost is not present and look for differences between the two corpuses, (for example, whether certain words and themes appear more often or less often depending on the presence of the ghost). One complaint that I have heard from other groups is that it is difficult to separate the text you are trying to examine from the rest of the corpus. Perhaps during Phase II, this a way in which Voyeur can be utilized. I am looking forward to Phase II because I find that the more text you have to analyze the more interesting your results and being able to analyze an entire act rather then just one scene should lead to more comprehensive results from Voyeur.

Using the Find Collocates Tool in TAPoR

This week, TAPoR has been working a lot better for nearly everyone. After playing around with this tool for a while, we were able to learn the strengths and weaknesses of TAPoR. Deciding that my favorite tool was the collocates tool, I decided to play around this with it, and to see if I could master it. With the taporware find collocates tool, you can look up a word that you might believe is significant, and see what words are used with that word most often. Fortunately, this tool was one that actually worked, and the results that it came up with were actually quite helpful. After playing around with this tool, tossing in random words to see different results that I would get, I decided to try to look for something a little more specific. While talking about Act three Scene four of Hamlet, my group and I discussed how there were many references to different senses, and uses of the words eyes and ears. So, branching out on this development, I used other tools first, like the word cloud and the list word tools, to decide which word would be best to look at. The word “sense” was used 7 times in this scene, compared to the word eyes that was used almost as often with six mentions. Deciding that these two words were very important, I looked up both words using the find collocates tool. To use the find collocates, the only thing you really have to do is type in the word you want to study, and pray that it works. These pictures show where you input the information, and the results I got from using the word “sense“, and then using the word “eyes“. As you can see, the word that has been used with the word “sense” the most often is the word “sure”, and the word “feeling” is the most common word connected to “eyes“. This information is a helpful start point, but unfortunately, it doesn’t help you to find meaning behind the words used together. The find collocates tool also, unfortunately, does not show us where these words are used within the act. To find them, we either have to guess the exact context, or find the use of these to words together some other way. Another thing that I wish this tool did, was tell me how many times the word “sense” and the word “eyes” were used. Although I know how many times in the scene it was mentioned from other tools, it would be nice to have that specific information included in the tool. Despite these few flaws, I do really enjoy using this tool. It is simplistic enough that a technologically incompetent person like me, can figure it out, considering all you really have to do is enter a word and press the submit button. However, as simplistic as it is to use, I also found it very helpful while trying to find themes within this act. Its definitely one of my favourite tools.

WordHoard, simply not a fan

WordHoard is very misleading at a first glance. It presents itself as this tidy, little, program that is going to super easy to use and will be a powerful tool to help you prove your arguments and thesis’. However, under closer inspection I found out that it’s in fact a limited and temperamental program. The more I dig into it, the more I feel like I’m becoming loss in an endless maze, not being able to find the information I require.

Continue reading

Monk: A Little Less Anger, A Little More Results

As seen in our first blog posts my teammates and I were having difficulties trying to work our tool.  Since then we have come together and rallied against the odds and have been able to scrap up some surprising results that I never expected us to get.

In my first blog post I mentioned my frustrations with Monk not allowing me to use many of the toolsets offered.  I showed a screenshot of the Analysis Tool denying me access because I hadn’t identified any “training data” or hadn’t “rated any items in the worksets”.  When I first read this I almost threw my computer against the wall in frustration. Thankfully I held back and took the more mature approach in trying to figure out what this limitation means.  If Monk is asking me create a training set and to rate my worksets, there must be a way of doing so.

In the Compare Worksets toolset, I had become familiar with differentiating between the Dunnings analysis methods.  In that drop down menu I had two other options that I hadn’t played around with: IDF First workset as training set, and IDF Second workset as training set.

Hey now, don’t I need to create a training set to use the analysis tool?

After thisrevelation I went ahead created my training set.  Based on discussions with my teammates (Monk seems to be a tool that is made for larger amounts of data, i.e. an entire genre of Shakespeare’s plays) I used Shakepeare’s corpus of comedies as my first workset, and his tragedies as my second workset.  An interesting tidbit I found while I was filling out the requirements was this:

These boxes were left blank when I used the Dunnings analysis method, but as soon as I selected the option to create a training set they were automatically filled in for me.  Why? I’m sure understanding this will help me gain more knowledge in figuring out what I can learn from this toolset, but for know it’s a mystery.

Once I’ve filled in the requirements I click compare, then continue. I save my results and return to my main menu.

Now that I’ve created a training set I need to rate my workset.  I use my Classification tool to individually rate my worksets.

NOTE: I can write down ANY WORD I WANT in the user rating column.  I have divided them into their genres, but my teammates and I have put down random words like love, death, and blood.  I originally thought that rating these plays helped the tool in it’s process, but since I am able to put any word I want, it seems that the classification is more for my sake.  I hope to make more sense of this in the near future.

Finally after all these steps I am brought back to the analysis tool. Now is the time to see what this tool can actually do!

Here is what I got:

Ah the sweet screenshot of results.

Now, I’m going to be honest here.  Although my teammates and I have been working to try and understand what the point of the decision tree is, I still don’t have a full understanding of it.  April has been working a lot with it, and she has grasped the statistical side of the decisions trees purpose.  I personally plan on spending more time with this tool, in trying to figure out how I can use it for Phase 2.  So please don’t be wary my Phase 2 teammates, you’re not stuck with “the girl who doesn’t know anything about her tool”, I’m going to figure it out, be patient.

Patience.  This leads me to my final comments about Monk.  Working with this tool wasn’t easy.  It took a lot of trial and error to figure out how to make what seemed like simple tools to work.  With some research we were able to discover that Monk is meant to work with larger sets of data, which would be great if that’s what we were assigned to do.  More information on my teams efforts to work with Act 3 Scene 4 and Monk will be discussed in our presentation, but for now I leave with a warning:


Voyeur: my treasured tool

Before I begin, I’d like to mention that Voyeur is probably now become the world’s easiest program to use in my eyes. I even find managing my way around ucalgary blogs to be more frustrating and confusing than the ability to run Voyeur. Like seriously, half the time i cant find the log-in page. But on a more serious note, this journey for me has been very exciting. I come from the lands of creating HTML pages and working with Photo editing programs. This right down my alley 😉

Since my last post, I’ve learned some minor things about Voyeur that has opened a few doors to further my analysis. This is where I wish I could go back to my previous post and hit the delete button! One of the biggest discoveries was the ability to search a word within “words in the entire corpus”, while receiving a list of all the results, instead of a specific word that shows up alone in the “words in document” bar.  This could have saved me a lot of comparing and searching for words in stage one! The good vs. good night issue I had in blog post one, can now be scratched out.

While struggling to determine the weakness(es) of our tool, we concluded that Voyeur didn’t have a help button, or an explanation page to assist users to better run the program. However, shortly after, we blindly discovered the tiny little question mark in the top left corner of every box. How did we miss that? I do not know.  Although that may seem silly, when clicking on the button, some group members found that they were led to a broken link. This could be that, Voyeur, like a lot of other tools being used in English 205, are picky with browsers. I never had any problems with my browser (Firefox).

In order to further appreciate what Voyeur has to offer, we looked at some of the other blog posts and tools. Again, I feel like since Voyeur is SO user friendly, that it is probably one of the better analysis programs since it offers a variety of both visual and concrete data. Everything that a user could possibly use is located on one screen. Convenient, I know.  There is no need to flip back and forth between screens, and even the boxes of data can be minimized.

I feel by being limited to only analyzing 3.4, our group is running in circles, looking to take on much larger chunks of the text. I think that Voyeur will be more useful in the next stage, because we will be able to compare ideas, themes, and characters on a much larger scale. We came up with a lot of neat ideas that could not be used in this stage, since 3.4 is only a very small portion of Hamlet.

The tools in the customizable template have been a topic of discussion.  Prior to today, as a group, we concluded that the extra tools are too similar, and not very helpful in our analysis. Kassidy had mentioned that he even attempted to google the purpose of these visual tools and how they worked. No such luck. Ruby posted a few screenshots of the extra visual tools using the text from 3.4 here. Although many comments have been made on the useless of the tool, I attempted to prove that these tools were more than just pretty to look at.  I wanted to figure out when in the text this knots occurred, and why they were looping and intersecting.  I decided on the words HMLT, GRTE, mad and madness to keep my scope very small.

What the heck does this mean? Well by looking at this screenshot, it looks like nothing but a children’s art project.  When you click on the different segments of the lines, information is brought up. The pop up tells you the context of the word, but it fails to mention who spoke that specific line. So where do I go from here? By breaking down these segments into easier manageable sections, I concluded that Gertrude stating “alas, he is mad” was the beginning of this mad debate ( we already knew this though). Following this, both mad and madness are continually brought up by Hamlet.

Note: I made the version on left so i didn’t need to keep click on each segment. I thought this would be an easier way to figure out what was going in.

One flaw to this specific visual tool is that I don’t think a user would be able to rely solely on these images. I used a lot of background knowledge in order to assure myself that these conclusions were correct or at least on par. These images are good for understanding basic connections on how words or perhaps conversations flow, but the amount of time it took to break down the knots allowing for a conclusion, was annoying. Another interesting thing to note is why is it that Hamlet and Gertrude’s circles are different sizes when we know they speak equally 25 times?  With that being said, I don’t know if I failed at attempting to figure these knots out, or if I was really making something out of nothing , but at least I can say I tried.

On a more positive note, I am very excited to see what Voyeur can offer for phase two of group projects 🙂


Monk: A Greater Understanding and a Bigger Hurdle

Since the last post, the Monk group has met twice. We have made significant advances with the tools of the program, but have also made a crucial and unfortunate discovery to humble our success.

Firstly; however, our discovery. In the “compare” toolset there is an analysis method that we has not managed to figure out before. It is called “IDF” and it allows you to select a training set. Once you manage to fulfill all of the options to the program’s liking, you are advanced to a screen much like any other one where you can select a work, view it and type in the concordance you desire. Most of the toolsets get to this page and end there. However, for this tool, you are allowed to take the workset you nominated as a “training set” (we recommend selecting the all-encompassing “plays: tragedies” and “plays: comedies” or something for the most options) and from there to re-select a mix of both full plays and even individual scenes and save it as it’s own workset. (Minimum 3 selections).

As usual you hit a dead end on the concordance page, but uniquely, your saved workset becomes useful. Take your new workset with its many parts and load it into the “Classification” Toolset.

From here you must give each document a rating and follow the continue button…

This is the part of the program that Monk specializes in. Naive Bayes and Decision trees. The explanation of which will be one of the major parts of our presentation. After selecting your method you can insert a prediction if desired and…. Voila! You get a complicated rating system of “confidence” and “frequency.”

Very cool – now for the sad part. This tool, from what we understand, is basically used for the identification and classification of author’s works. It particularly focuses on entire play and their characteristics. Poor little Act 3, Scene 4 does not much register in the scale, and the part that does we of course already know its origin and the characteristics of it as a Shakespeare play. So how can we use Monk’s most defining tool as an aide in discovering Act 3, scene 4? That is our current mission. As well as explaining to you all this lovely piece of analysis:

Also since our last posts we have done more research into the purpose and uses of Monk…
We found out that Monk is one of the first of the Digital Humanities programs, almost a prototype for Wordhoard. Through different group member’s findings we have determined that the Classification, Frequency of words and the Concordance searches are specifically meant for analyzing large scale works such as entire plays or collections to find themes throughout historical moments, between writers or characteristics of the writers themselves. As it is, we are not sure how useful it is as a tool to analyze one scene in one play. Our greater understanding of the tool itself has further clarified this. Monk is great at finding certain things within a text, any text, of any size. Although, when it comes to comparing them, it is harder for a small document such as a scene to provide enough information to represent itself against other documents.

For the remaining days, our work will be centered on figuring out how Monk can directly provide insight into Act 3, Scene 4 specifically, and to see if it if possible to use the tool in any depth without comparing the scene to the entire works of Shakespeare’s tragedies –
Because as interesting as our tool can get, our focus must be on the one scene, and we are trying to be optimistic about getting it to work for us!

So, till next time, I leave you with this excerpt from the Monk help buttons.

Using the List Words Tool to Begin Anlyzing Act 3.4

TAPoR has a wide variety of tools that perform various functions, though not all of them are helpful in analyzing Act 3.4.  As a result, our group decided to each pick one tool in TAPoR that we found particularly interesting or useful, and use it to examine Act 3.4.  The tool that I choose is List Words.  It does exactly as the name implies, it takes all the words in a document and lists them according to frequency.  I thought that this would be a useful way to examine the speeches of Gertrude and Hamlet separately before comparing them.

In Jennifer\’s blog post last week, she made note that WordHoard is a hypothesis-testing machine due to the specific way in which it functions.  For opposite reasons, the List Words tool in TAPoR is a hypothesis-generating tool. It is a good place to begin on an examination of the act because it takes into account the entirety of the document and displays results in a linear, easy to read format.  However, you are not able to identify the context of the words.  To do that you would have to then input specific words in to the “Collocates tool.”

One of the strengths of the list words tool is that it easily eliminates words like “it,” “as,” “a,” which are referred to as “Glasgow” stop words, making the results a lot more manageable to look at.

Tool Broker Window for List Words

A weakness is that it does not eliminate speaker indications and stage directions.  To ensure that those words did not turn up in my results I had to manually create a special document that included only the lines of speech.  I did this my simply copy and pasting results of the XML extractor in a word document, manually deleting the parts I didn’t want, and then saving the document in a plain text format. This process worked successfully and gave me the following results when used with the tool:\

Gertrude's lines on the left, Hamlet's lines on the right.

From these results I started to make conclusions in regards to the relationship between Hamlet and Gertrude.  The first thing I noticed was that Gertrude references Hamlet by using “thou,” “thy” and “Hamlet” a total of 17 times, as opposed to “you” which is used only 8 times.  (I got the results for “you” by changing the search parameters on the “Words limited to” space to “All words” because “you” is one of the Glasgow stop words omitted by my first search).  On the other hand, Hamlet addresses his mother using “you” a total of 37 times and “mother” 7 times.  These results suggest that Gertrude is a lot more formal towards her son, while Hamlet is a lot more familiar.  As such, Hamlets continuous addresses of “good mother” and “you” are used as a sign of disrespect, displaying his shame at her recent marriage to Claudius.

Another thing I noticed was the use of verbs by the two characters.  For instance, the verbs that Gertrude uses multiple times include “speak” and “come,” while Hamlet uses verbs like “make” and “look” the most.  I believe that this quantitative examination of word usage is indicative of the characters motives in the scene.  While Gertrude’s motive is to convince Hamlet to disclose the reason for his strange behavior, Hamlet’s intention is to make Gertrude feel guilty by forcing her to reflect on her actions over the past few months.

Overall, List Words is a fairly useful tool.  It has shown me the difference of tone and motive in the two characters, but to gain further understanding of the scene I would have to use List Words in conjunction with the other tools that TAPoR offers.

Battle with WordHoard? Challenge Accepted

I rescind my earlier statement. The greatest limitation to WordHoard is not its user. It is definitely the fact that to get any results, you almost need to know exactly what you are looking for. This is problematic when you have a big, general question to ask and are trying to find smaller threads of thought to follow.

Luckily, I didn’t have a really big general question. My group and I started out by thinking of a general question from which we could each follow individual questions and then compile our results to answer the big question. Solid plan. If only it was that easy.

I’m exploring if/how Gertrude acts differently towards Hamlet when Polonius is in the scene vs when he’s dead. After tackling WordHoard until it submitted to my searches, I became quite hopeful about getting results. Before sitting down in front of my laptop, I compiled a list of words to search, thinking it would be easy. Type in the words, select gender, scene, etc to narrow down my search, get some good results, go to my group meeting this morning shining with pride at my achievements and masterment of WordHoard. Nope. Every word I had brainstormed about being helpful to find yielded no results. I became quite familiar with the “0 results” screen.

Okay, time to get creative. I started randomly messing around on WordHoard (clicking buttons and searching for things under the dropdown menus that I didn’t understand, such as the “xx”, “vv”, etc.). This also gave me zilch. Right. Got to start deeper thinking. I refuse to let this program stymie me.

How to see if Gertrude reacts to Hamlet differently? I could look for tone. Alright. How do I search for tone when WordHoard only searches words? I need positive and negative words. Yes, this makes sense. However, there are no really distinguishing words for being positive used in Shakespeare. But I can search “not”, and I did.

By comparing these results, I can tell that Gertrude is neither more or less negative before or after Polonius dies, as is Hamlet. So her son being a murderer does not send her into despair. Good that I’m finally getting somewhere with WordHoard, however this isn’t particularly helpful, as reading the text tells me much the same. Only here it is broken down into exact numbers.

On to another vein of thought. What happens when I search how many times someone says “Hamlet”? I get this:

Except for the highlighted line (said by the Ghost), Gertrude is the one saying “Hamlet”. So she says it five times. Not particularly great results on its own. But, WordHoard does provide context for every searched word. Now, looking at how Gertrude addresses Hamlet/ speaks to him around saying his name, there is a better idea of how she feels towards him. When Polonius is still alive, she questions him, as Polonius expects her to. After Hamlet kills him though, it is interesting to see that she refers to him as “sweet Hamlet” or “O, Hamlet!”. Not the words of a mother horrified about what her son has done, which corroborates my earlier findings with “not”. So far, so good. Also, to answer my question, there is a definite difference between how she treats him with Polonius in the room and with him dead. Without him in the room, she seems to be more openly affectionate with him. The question now is what type of affection? This is hopefully going to be answered by the corroboration of mine and my group members efforts.




Putting aside preconceived notions and discovering something useful

As my initial process with Voyeur comes to a close (or rather a new beginning) I can now securely say that I have entered into the world of digital humanities and embraced a new way of analyzing text.  Referring back to Katy’s first blog post of the traditional “cookie-cutter” method of analyzing text (go to Katy’s blog post here:\”Momentary Panic and Gradual Acceptance\”), I felt a little uneasy venturing into this unknown world of digital humanities.  I had no faith in my computer skills or how any of these tools would help me analyze text.  Now looking back, I have realized that suffering the long and tedious process of going through a text with only a pen or pencil in hand, is not the only option!  I find it ridiculous that I actually thought that the traditional method was easier. It was only easier, in my mind, because it was all that I knew.  I tested the water of digital humanities first with Wordhoard and was intrigued that I now possessed a single program on my computer that would instantly take me to any Skakespearean play I needed.

Don’t need to carry you around anymore! Ha Ha! :

But, I never took the time to make new discoveries about WordHoard and found it visually unappealing.  I gave up just as easily with the other tools; I assumed they would be just as uninteresting – and of lesser use.  Surprisingly, I ended up with Voyeur as my tool, which I knew least about.  Like I said in my previous blog post (check out my first blog post here!: “Initial Responses to Voyeur“), I thought it was only a bubbleline chart.  Yet now I was forced to look at this tool, figure out its purpose, and find a way to use Voyeur to help me discover new things about Hamlet.  And it wasn’t easy – until I let it be that is.  Once you find the right browser (avoid using Chrome and Safari – for Mac users) and get over the glitches of Java (as Nicole, my fellow group member will tell you, “it’s not your fault, it’s Java’s”) Voyeur has become one of the most useful online tools I have ever come across.

One of the major discoveries that I came across with Voyeur was that I realized it will take me to direct themes within the play.  My favourite tools became the Word Cloud, Word Trends frequency chart, and the Words in the Entire Corpus tool:

I began to correlate these three tools into finding different themes within Hamlet and how the terms were related according to how many times they occurred together or apart and so on.  When I was fiddling around with the program, I was inspired by Katy’s idea of taking a modified version of 3.4 and uploading it onto Voyeur.  I decided to go onto Sparknotes and then proceeded to create a copied and pasted document of 3.4 in the modern text version (check out No Fear Shakespeare for Hamlet).  I then compared the major terms in both versions, and also uploaded both at the same time and compared the two.  I am still looking deeper into this but what I have concluded so far is that the concept of “good” versus “evil” is a more evident theme in the modern text including the words “virtue”, “heaven”, and “devil”.

When you notice the repetition among certain terms and how they interlace you can then start asking deeper questions like I did by comparing the original and modern texts.  TAPoR is another tool that is similar to Voyeur where there is a word count (and other things I don’t know about yet until the group presentations!) but without the visual components.  For me, as a visual learner, the visual components are what make Voyeur special and interesting to play around with.  However, there are definitely some tools on Voyeur that are unnecessary.  If you didn’t see my previous post called “Are these necessary?” (check it out here!: “Are these necessary?“), I will explain – some of the tools are quite repetitive and appear almost “complicated” because Voyeur already has other tools that do the same thing in a more clear manner. For example, these tools (Word Fountain, Lava, and Knots):

all seem hard to read and understand.  Some of the comments I received on my previous post about these tools said they are visually appealing (maybe) but agreed that they are hard to understand.  So why have them?  Perhaps I should keep an open mind but so far I don’t see their significance!  As a group, we Voyeurans (can that be a word now?) found little use for not only the above tools I just mentioned but also some other visually confusing and also repetitive tools on Voyeur.  There is always room for improvement when it comes to technology.

Another day, another new discovery

I can now say that I have spent a considerable amount of time on WordSeer, and am (finally) beginning to get the hang of it. Although I will stand by my first post and once again state that WordSeer is a simple-to-use tool, it also has its challenges. The main issue that our group has noted is we cannot seem to isolate a single scene within Hamlet, and therefore have had problems when comparing 3.4 to the rest of the play. This has especially presented us with the challenge of integrating 3.4 into our presentation. If there is a simple explanation for this problem—which I am sure there is—I would be forever grateful!

Another feature I have just discovered (although why it took me so long—since this is a word analyzing tool—is a mystery to me), is Read and Annotate. It allows the users to read, highlight, and take notes, within any piece of writing on the site. Some may say, “Why not just use the actual book?” Well, for me, the answer is simple: my handwriting is terrible, and I often spend more time decoding my own words than I take to read the entire play. The Read and Annotate keeps a neat and organized collection of your notes, while allowing you to compare other works at the same time.

Another feature that was just discovered—many thanks to Richelle—is the Newspaper button. This allows you to search a word, hit Newspaper, and have the word appear on the a Heat Map. Super convienant!

These are just a few of the newly discovered aspects of WordSeer, since collaborating as a group and beginning our final presentation. One thing that has been talked about during our group meetings has been the question: Is WordSeer more a qualitative or quantitative tool? After a lengthy discussion on the topic—and a few awkward silences—we came to the conclusion that it involves both aspects. Searching for words and being presented with a list of results is a helpful quantitative tool. We can easily compare word frequencies within Hamlet and compare it to other plays written by Shakespeare. In contrast, WordSeer is also qualitative when receiving results and choosing which words are of importance within the scene. I think this is what makes WordSeer so unique; it provides multiple questions and observations that assist the users in creating hypothesis.

With all of the challenges I have encountered and hours that have been spent on WordSeer, I will say this: I am extremely happy that the entire corpus of Shakespeare is readily available for use, making it all the more faster when searching within a text. For this reason WordSeer is a great tool for future use, especially other Shakespeare courses. Thanks Aditi Muralidharan!

This has been a long week—with many early mornings—but overall I would say the results have been worth it. I have learned so much about WordSeers capabilities and how the tool works. However, my findings have not just been limited to WordSeer, but reading other classmates posts and comments, I have begun to understand more about text analysis tools and the Digital Humanities in general. I am looking forward to the presentations!

A Slight Success With TAPoR

*Edited to correct a mistake in interpreting the use of ‘thou’ and ‘you’. My apologies, and thanks to jenniferbist for pointing out my flaws.*

In my last post, I did a bit of complaining on the subject of how I find TAPoR restricting and limited in its use of word lists. I’ve tried to move past my initial frustration and to proceed with an analysis of Act 3 scene 4 of Hamlet in an attempt to find what I have decided to set out to do, mainly:

  • What is the theme/ mood of this scene?
  • What is the relationship between Hamlet and Gertrude?

To find answers, I launched right into entering the scene into the list word tool to isolate and sort through the words used and their frequency. From my own reading of the scene, it became apparent that the main focus is based on a confrontation. The list of words gives me a result that lives up to this idea:

As is shown, the word ‘thou’ (and its related ‘thy’) is the most used. I find this relevant because the usage of ‘thou’ is one with a sort of personal note, with a sense of being involved in closer relations than ‘you’ gives. This use of ‘thou’ is something that already gives me a feel for a mood- it is a serious conversation where there is an attempt to be personal in pleas.

A frequent word which points me to a possible theme is the use of the word good:

What I first notice is its distribution: not only is Hamlet the only character to use it, but it is used in higher frequency near the end of the scene.

Another interesting thing I found is that Hamlet’s primary use of ’good’ is to refer to the night:

Now, these discoveries bring up new questions for me about the scene: why is the adjective used frequently near the end of the scene? Why is Hamlet the only character to use it? What is the purpose of this repetition of ‘good night’. I don’t have an answer to this from the tool alone, but it has allowed me to find these details which I had not previously noticed. With these results in mind, I may go back to the text in a hope to find more there.

For the analysis of the relationship between Hamlet and Gertrude, I resort to comparing how these two characters address one another.

In looking at how Hamlet addresses his mother, he never refers to her directly by her name, but rather by her titles of ‘Mother’ and ‘Lady’:

An interesting thing I notice (again) is Hamlet’s use of the word ‘good’; he uses it twice to refer to Gertrude:

These results show me that Hamlet is communicating to his mother in a formal way.

Gertrude is informal in her adressing Hamlet at the start, using ‘thou’:

She is the character who uses the lemma ‘thou’ the most, being the personal character in the scene. The distribution drops off and it seems she becomes much less informal, communicating to her son in pleas and using his name personally. Gertrude’s use of adjectives towards Hamlet are few, using ‘sweet’ and ‘gentle’:

Also shown is that Gertrude does refer to Hamlet in addressing him with an “Oh!”, in a voice which reminds me of plea. What she communicates in those lines shows that indeed, she is making a plea:

The results I pull from the scene are lovely to find. They are limited, yes, but the word lists provided allow me to focus on specific words and results I will look at again in another close reading of the scene, allowing my mind to be enlightened a little more than it was before. I can see that the scene does start out rather formal in the relationship between the two characters, shifting slowly to a more personal tone.

Hamlet’s madness via TAPoR

The shift in tone and demeanor of Hamlet within Act 3 scene 4 is fairly easy to notice. In the opening stages of the scene he is forceful and confidently lecturing his mother. By the scenes end he seems disoriented, and intensely trying to defend his sanity to his mother and get out as soon as possible. This shift occurs directly after the ghost has exited and Hamlet is coming to the realization that only he was able to see the king’s ghost (unlike the first time the apparition appeared). The timing of Hamlet’s shift in demeanor indicates a crucial moment for conclusions (for or against) regarding his madness – as the ghost is definitely a central figure to this argument.

The short questions Hamlet directs at his mother seem to be a sobering moment for him: “Do you see nothing there?” (3.4.133) “Nor did you nothing hear?” (3.4.135).  Is he questioning his own sanity here? Sparknotes, in the 3.4 summary, suggests Hamlet is desperately trying to convince his mother that his madness is an act. They go on to state that this is a “point in the play where audiences and readers have felt that there is more going on in Hamlet’s brain than we can quite put our fingers on”. TAPoR directed me to this desperate speech of Hamlet’s while using the highlighter tool to search for “madness” – he states it 3 times within only a few lines; This repetition indeed comes off as “desperate” – and desperate perhaps because it is not just Gertrude he is trying to convince, but also himself.

Following this, Hamlet is flustered, as if his thoughts are somewhere else. His mother asks about her divided heart, and Hamlet seems to respond in a rather uninterested way: “Oh, throw away the worser part of it, / and live the purer with the other half. / Good night.” (3.4.160-162). This response is a far cry from the Hamlet of just a few moments ago who was vehemently trying to force his mother to notice the godlike features in Hamlet Sr. that Claudius lacks.

Hamlet then (he seems to forget that he just stated “good night”, as if to leave) goes into a rant asking his mother to stay out of Claudius’ bed. He then utters “good night” 3 more times before actually exiting the scene, each with haphazard thoughts thrown in between. In the latter half of this scene we see Hamlets lines are far different from the forceful, and confident ones he uttered when he first entered. While he is still at times passionate, and definitely just as shocking as his earlier lines had been, there is something different. He has become introverted, and, as the several utterances of “good night” indicate, he is urgently trying to leave. This is a pivotal moment with the play in which we see Hamlet himself seriously questioning his sanity.

I have intentionally left out any mentions of TAPoR (besides the highlighter tool) up until this point for two reasons:

  • As my fellow TAPoR colleague, Kira, pointed out in her first blog post, “the tool is pulling my focus away from the text I am analyzing”. Upon finishing my first blog, I realized that I had barely read from the text itself. I wanted (and felt it necessary) to come to conclusions with a healthy amount of quotations directly pulled from the text. This leads to my next point.
  • The second reason TAPoR hasn’t been mentioned that much, is because I didn’t use it to directly come up with any of the thoughts I have mentioned.

As contradictory as it may sound with the above statement, TAPoR was still a crucial piece of engaging with the text as much as I have. This stream of thought would not have played out had it not been for my intrigue upon first seeing the distribution of the word “good” in the List Words tool as pictured below.

The reason “good” occurs so much in the latter stages of this scene is, of course, because of Hamlets consistent (and disheveled) stating of “good night” before jumping right back into a rant.

To paraphrase something Professor Ullyot mentioned*, the way TAPoR worked most effectively for me in this analysis, was simply as a new platform to produce thought provoking ideas. The quantitative analysis produced from TAPoR may have not been very in depth (I am only a beginner after all) but it did produce some extensive close reading and qualitative thought. TAPoR may not have been present the whole way through my working out of Hamlet’s madness within this act, but it definitely helped stir my interest and come up with an argument.

After this analysis I would conclude, based solely on the evidence gathered from Act 3 Scene 4, that Hamlet is slipping/has slipped into madness and is not just acting.


* I think he stated something along these lines. I thought it was in a blog post, but I could not find it anywhere… maybe a class discussion? If anyone can remember let me know, if not, Professor Ullyot, sorry for putting words in your mouth!

Are these necessary?


I think my fellow Voyeur group members will agree, but the Knot tool, Lava tool, and Word Fountain tool (above) seem quite useless compared to the other tools on Voyeur.  I guess it provides an alternate visual representation of the text but to me it seems unclear and visually “messy”.  What do you guys think?

Monk… One Step Forward Two Steps Back

Monk Blog Post-#2

Since my last association with Monk we have gotten off on better terms. I have learned that Monk is a limited tool and not to expect it to do these extravagant things because that is not what it is built for.  The primary use of Monk is that is a word counter, it locates words and notices the popularity with them and the concordance that occurs. It is also used to compare the words between other parts of text on a larger aspect, the larger the comparison the better results you get from it.

I have tried to upload the text that other classmates have extracted from their tools however I have been unable to do so. Monk freezes up and does not let me bring it up myself. I have tried switching internet users (Google Chrome, Firefox and Internet explorer) and that did not work. It was another let down because I thought that it would be a neat experience to upload text and look at them on a whole while focusing primarily between what was said in the speech while focusing on the text that was needed. Unfortunately that could not be done so it was best to try and work with what I had got.

I have learned the use of Monk and how it can be helpful if you compare on a larger scale. If you look at three different works and comprise them all together you can save them as a workset. From there you can compare the concordance between all three of them and see which words are common among them.  I have been able to look at Hamlet 3.4 with comparison to other texts as well as other groups of plays and works written by authors.

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Narrowing the Scope: Zeroing in on Word Seer and the Digital Humanities

Dane Thibeault

Phase 1 Blog Post 2: Narrowing the Scope: Zeroing in on Word Seer and the Digital Humanities

I am writing this post to address some reassessments that I have made to my opinion of word seer, while additionally describing what role I had to play in the team project process to date. I am one apt to revoke my opinions of something if I find a cause for concern, and in regards to some of the functions of word seer, I experienced a few less than praise worthy difficulties during my research, which I will go into in detail throughout the duration of this recollection, while offering some insights on my assistance towards my team’s efforts.

Throughout the course of my team’s research in exploring the functions of the tool word seer, I have contributed a great deal to the overall process in individually assessing the tool’s potential to return relevant and insightful results from simply inputting data. In my previous blog post, and during my overall involvement process, I posed this fundamental question to myself: To what extent is word seer interesting, and to what extent is word seer insightful? What I mean by “interesting” is that many aspects of the tool may prove visually appealing or intriguing(Such as the feature depicted in the image below—yes, it is interesting to look at, but what does it really tell us about Hamlet?), yet, what I search for is the
more “insightful” qualities of the tool, such as what it can tell us that we could not readily or as easily identify through traditional text analysis, and how it is effective within the broader spectrum of the English discipline, altogether.

How I went about this process, the preliminary stages having been discussed in my previous blog post, evaluating to a lesser extent the same fundamental issue, was by exploring the capacity of the image comparative functions available in word seer to accurately represent trends or patterns that reveal details about the text of Hamlet, and the narrower study of act three scene four in particular, in a way that is either absent or otherwise complicated by traditional critical text analysis and closed reading. I concluded my last blog post with aspirations to further assess the word tree feature in particular, yet, I have concluded since then in my individual research, and as part of my contribution to the collaborative effort of the project, that this function is largely aesthetic and quantitative, and while it suggests word frequencies, it does little to suggest new avenues of research and interpretation regarding the text.

Therefore, seeing as to this disillusionment I experienced in my individual testing of the word tree function, I was further prompted to explore the more fundamental aspects of word seer, the words “described as” feature. What I was both surprised and disappointed to uncover, in a search of Claudius described as “blank”, was that both incoherent and unexpected results surfaced (pictured below, and keep this question in mind—does this sound like the villainous Claudius?) contributing to a mounting scepticism on my behalf.  As a result of this development, I felt compelled to shift my individual efforts and research more towards considering the overall impact of the tool, from the broader perspective of its impacts on the digital humanities, as opposed to its individual features, which I consider to be obscure and highly perplexing when I am working with them on my own. What on earth is a “snippet”, and how does one create of these, anyhow?

So, what is the overall significance of the word seer tool, which I initially felt to be its image qualities? My answer: I have no idea as of yet. In disarming myself of the ammunition that this tool is one of the rare few that can achieve qualitative value in hinting at the aspects of theme within a text, I was left with little to move on. However, I was able to reassess my priorities, and refocus my efforts. So how have I really individually contributed to the group work process, then? Simple: I started doing what I do best. What is that you ask? I simply grabbed a blank sheet of paper and a pen, and began to draft an outline, answering questions that I began to pose for myself, such as “why use word seer instead of other tools?” and “how could what I am trying to do with word seer be resolved better by either using other digital humanities tools or traditional text analysis?” and soon found that I was less overwhelmed about the whole process. I proceeded to complete my outline, jotting down some of the problems I faced, the lingering questions I had, and points that I felt would be effective to include in the presentation for the project, and promptly brought the outline with me to today’s group meeting to share with my team members. They were actually impressed, and were intrigued by many of the questions I raised, which has led me to conclude that there is a place for tradition in the rapidly advancing field of digital humanities, and additionally caused me to consider this underlying question: to what extent is there a potential to integrate traditional critical text analysis approaches, and the digital humanities? This is a question I have yet to provide an answer for, yet, I am determined to do so as my next ambition.

What ever happened to you, old friend?

A Better Understanding

Alright – second blog post here we go.

Since my previous post, I have gotten to know Wordseer a little better and think I may be able to provide a little more insight into this tool.  The group meetings I have been participating in with the other Wordseer “experts” have really helped all of us, I think, develop a deeper knowledge of exactly what it is this tool has to offer.

Diving right in, I want to show you guys some new features I have discovered.

If you have tried your hand at Wordseer, you already know the basic functions it offers for thorough text analysis. These sub-tools include: searching comparisons through “described as” or “any relation to”, “done by” etc. functions.  Other searches offered are Heat maps and Word Trees – which provide a visual element to analysis…blah blah blah…all of this has been covered in detail in my last post – which I know you all read…anyway…

What I have not shared with you is the nifty way you can compare different words within Heat maps! Intriguing, right? Prepare yourself.

Pretty cool, right?! In this particular screen shot, I am comparing FIVE words used within Hamlet with each other. In the column to the left of the map, you can see the words I chose to compare (war, kill, die, death, and revenge). Moving to the right to actual Heat map, the distinction between words is marked by the color. Something interesting I noticed on this map is the difference between the usage of the words “Kill” and “death” – with the first column representing “death” and the final column representing “kill”. Why is there such a difference in the amount of times each word is used? Does it mean anything?

This is the kind of information DH tools are excellent at providing.

Something else to note about the Heat Maps is that when a user has their curser on a colored tab, as I do in the image below, the specific instance in which that word is used will appear to the right of the tab, providing users with the entire line.

This kind of information can be helpful for users while trying to determine the mood, or tone in which a specific word has been used.

Another cool feature of Wordseer is the “Read and Annotate” function available.  I have found that this aspect of Wordseer is reminiscent of traditional analysis methods in that you are able to read the text and “highlight” among other cool tricks.  See for yourself….

Using an example from Act 3, Scene 4 of Hamlet, you can see how handy this tool really is.  By highlighting and clicking on any word, a box will appear with a list of options. By clicking the “Newspaper-strip Visualization” option another box will appear to the right with the highlighted word. And when you click “Go”…..

You are brought to a new screen featuring a Heat Map including every instance of the word, in my case “offended” used in the entire Shakespeare corpus. This of course can be manipulated to feature one play exclusively of anything else you want – including Word Trees.  Is this particular information useful or insightful, maybe, maybe not…is it cool…umm yeah!

One last tool offered under the “Read and Annotate tab is the “Related words” option located in the same menu displayed when you click on a highlighted word.  Selecting “Related Words” pops up another box, providing users with “Nearby Adjective’, Nearby Nouns”, “Nearby Verbs”, as well as words used in a similar context.

This can be helpful for users attempting to strengthen a hypothesis they may have or further develop initial ideas.

With these new discoveries in Wordseer, I am feeling more are more comfortable “experimenting” with my own theories.  Overall, I would call Wordseer incredibly user friendly…once it decides to accept your friend request!

Starting Out With Voyeur

I came into this class with little to no knowledge about the use of digital tools to analyze texts.  To be honest, upon hearing about the use of digital tools in the humanities, I was a little bit skeptical of the idea because I was rather unsure of my ability to understand these tools and my ability to use them.  In using the Voyeur/Voyant tool, I have discovered that it is very user friendly.  For someone such as myself who has difficulty understanding many things on the internet, Voyeur is a surprisingly easy and user friendly tool.

By experimenting and basically just playing with the tool to discover what it can and cannot do, I have found Voyeur to be a tool with many facets, there are a number of different tools that can be used in voyeur for the purpose of passage analysis.  Items such as the word frequency chart, and the knot tool among others are there for people who possess a more visually oriented learning style; on that not however, Voyeur is not limited to people who are visually oriented, it also has tools such as term frequencies for both corpus and documents, as well as document KWICs (Key Words In Context).

Above are examples of Voyeur’s capacities for both visual analysis and written analysis of a text, in this case I was using both of these tools to compare Hamlet and Gertrude’s reference to his father.  The top tool seen is the KWIC tool and it is showing the words surrounding the word father, showing the context around the word, and allowing for a quick and easy analysis by a person.  The bottom tool shown is the Word Knot and it is showing where the words, Thy, My and Father overlap.  The Word Knot is a useful tool, but overall, my group has found the frequency chart to do much the same thing and is also easier to grasp.  Due to the work done so far in phase 1, I have gone from being quite skeptical about the idea of digital analysis, to being willing to try it and finding that it is both useful and enjoyable.

About the Developers

A few people have asked about the contact information for the developers of our various tools. As I said in class, remember a few things before you contact people for help:

  1. Describe your problem in detail, and ask clear and focused questions. Tell them what steps you have taken to try to resolve it yourself.
  2. Be polite and deferential. They are not customer service agents, but professors and experts who have devoted a lot of time to developing these tools and making them freely available to us.
  3. Give them at least 48 hours to respond; if you have nothing by then, take that as your answer or just keep waiting. Don’t send a follow-up for at least a week.
  4. Thank them for their time.
  5. Link to the course blog in your e-mail.

The Developers

Feel free to add other names of helpful people you’ve contacted in the comments; just make sure you tell us which program they were helpful about.


  • Geoffrey Rockwell has a contact page on his blog. He is also on Twitter.
  • Stéfan Sinclair also has a contact page with a form, and here is his Twitter profile.
  • Martin Mueller is the contact person; you can e-mail him directly from the home page.
  • Rockwell, above, is listed as their main/only contact.
  • Kamal Ranaweera <kamal.ranaweera {at}> manages user accounts.
  • Aditi Muralidharan’s blog has her e-mail and Twitter details.

Gertrude’s Lines from Act 3.4

I’ll warrant you, fear me not. Withdraw, I hear him coming. Polonius hides behind the arras Enter HAMLET.

Hamlet, thou hast thy father much offended.

Come, come, you answer with an idle tongue.

Why, how now, Hamlet!

Have you forgot me?

Nay, then, I’ll set those to you that can speak.

What wilt thou do? thou wilt not murder me? Help, ho!

O me, what hast thou done?

O, what a rash and bloody deed is this!

As kill a king!

What have I done, that thou darest wag thy tongue In noise so rude against me?

Ay me, what act, That roars so loud, and thunders in the index?

O Hamlet, speak no more: Thou turn’st my eyes into my very soul; And there I see such black and grained spots As will not leave their tinct.

O, speak to me no more; These words, like daggers, enter in my ears; No more, sweet Hamlet!
No more!

Alas, he’s mad!

Alas, how is’t with you, That you do bend your eye on vacancy And with the incorporal air do hold discourse? Forth at your eyes your spirits wildly peep; And, as the sleeping soldiers in the alarm, Your bedded hair, like life in excrements, Start up, and stand an end. O gentle son, Upon the heat and flame of thy distemper Sprinkle cool patience. Whereon do you look?

To whom do you speak this?

Nothing at all; yet all that is I see.

No, nothing but ourselves.

This is the very coinage of your brain: This bodiless creation ecstasy Is very cunning in.

O Hamlet, thou hast cleft my heart in twain.

What shall I do?

Be thou assured, if words be made of breath, And breath of life, I have no life to breathe What thou hast said to me.

Alack, I had forgot: ’tis so concluded on.

Hamlet’s Lines from Act 3.4



Now, mother, what’s the matter?

Mother, you have my father much offended.

Go, go, you question with a wicked tongue.

What’s the matter now?

No, by the rood, not so: You are the queen, your husband’s brother’s wife; And — would it were not so! — you are my mother.

Come, come, and sit you down; you shall not budge; You go not till I set you up a glass Where you may see the inmost part of you.

Drawing How now! a rat? Dead, for a ducat, dead! Makes a pass through the arras.

Nay, I know not: Is it the king?
A bloody deed! almost as bad, good mother, As kill a king, and marry with his brother.

Ay, lady, it was my word. Lifts up the arras and discovers Polonius. Thou wretched, rash, intruding fool, farewell! I took thee for thy better: take thy fortune; Thou find’st to be too busy is some danger. Leave wringing of your hands: peace! sit you down, And let me wring your heart; for so I shall, If it be made of penetrable stuff, If damned custom have not brassed it so That it be proof and bulwark against sense.

Such an act That blurs the grace and blush of modesty, Calls virtue hypocrite, takes off the rose From the fair forehead of an innocent love And sets a blister there, makes marriage vows As false as dicers’ oaths: O, such a deed As from the body of contraction plucks The very soul, and sweet religion makes A rhapsody of words: heaven’s face does glow o’er this solidity and compound mass, With heated visage, as against the doom, Is thought-sick at the act.

Look here, upon this picture, and on this, The counterfeit presentment of two brothers. See, what a grace was seated on this brow; Hyperion’s curls; the front of Jove himself; An eye like Mars, to threaten and command; A station like the herald Mercury New lighted on a heaven-kissing hill; A combination and a form indeed, Where every god did seem to set his seal, To give the world assurance of a man: This was your husband. Look you now, what follows: Here is your husband; like a mildewed ear, Blasting his wholesome brother. Have you eyes? Could you on this fair mountain leave to feed, And batten on this moor? Ha! have you eyes? You cannot call it love; for at your age The hey-day in the blood is tame, it’s humble, And waits upon the judgement: and what judgement Would step from this to this? Sense, sure, you have, Else could you not have motion; but sure, that sense Is apoplexed; for madness would not err, Nor sense to ecstasy was ne’er so thralled But it reserved some quantity of choice, To serve in such a difference. What devil was’t That thus hath cozened you at hoodman-blind? Eyes without feeling, feeling without sight, Ears without hands or eyes, smelling sans all, Or but a sickly part of one true sense Could not so mope. O shame! where is thy blush? Rebellious hell, If thou canst mutine in a matron’s bones, To flaming youth let virtue be as wax, And melt in her own fire: proclaim no shame When the compulsive ardour gives the charge, Since frost itself as actively doth burn And reason panders will.

Nay, but to live In the rank sweat of an enseamed bed, Stewed in corruption, honeying and making love Over the nasty sty,

A murderer and a villain; A slave that is not twentieth part the tithe Of your precedent lord; a vice of kings; A cutpurse of the empire and the rule, That from a shelf the precious diadem stole, And put it in his pocket!

A king of shreds and patches, Enter Ghost. Save me, and hover o’er me with your wings, You heavenly guards! What would your gracious figure?

Do you not come your tardy son to chide, That, lapsed in time and passion, lets go by The important acting of your dread command? O, say!

How is it with you, lady?

On him, on him! Look you, how pale he glares! His form and cause conjoined, preaching to stones, Would make them capable. Do not look upon me; Lest with this piteous action you convert My stern effects: then what I have to do Will want true colour; tears perchance for blood.

Do you see nothing there?

Nor did you nothing hear?

Why, look you there look, how it steals away! My father, in his habit as he lived! Look, where he goes, even now, out at the portal! Exit Ghost.

Ecstasy! My pulse, as yours, doth temperately keep time, And makes as healthful music: it is not madness That I have uttered: bring me to the test, And I the matter will re-word; which madness Would gambol from. Mother, for love of grace, Lay not that flattering unction to your soul, That not your trespass, but my madness speaks: It will but skin and film the ulcerous place, Whiles rank corruption, mining all within, Infects unseen. Confess yourself to heaven; Repent what’s past; avoid what is to come; And do not spread the compost on the weeds, To make them ranker. Forgive me this my virtue; For in the fatness of these pursy times Virtue itself of vice must pardon beg, Yea, curb and woo for leave to do him good.

O, throw away the worser part of it, And live the purer with the other half. Good night: but go not to my uncle’s bed; Assume a virtue, if you have it not. That monster, custom, who all sense doth eat, Of habits devil, is angel yet in this, That to the use of actions fair and good He likewise gives a frock or livery, That aptly is put on. Refrain to-night, And that shall lend a kind of easiness To the next abstinence: the next more easy; For use almost can change the stamp of nature, And either …the devil, or throw him out With wondrous potency. Once more, good night: And when you are desirous to be blessed, I’ll blessing beg of you. For this same lord, Pointing to Polonius. I do repent: but heaven hath pleased it so, To punish me with this and this with me, That I must be their scourge and minister. I will bestow him, and will answer well The death I gave him. So, again, good night. I must be cruel, only to be kind: This bad begins and worse remains behind. One word more, good lady.

Not this, by no means, that I bid you do: Let the bloat king tempt you again to bed; Pinch wanton on your cheek; call you his mouse; And let him, for a pair of reechy kisses, Or paddling in your neck with his damned fingers, Make you to ravel all this matter out, That I essentially am not in madness, But mad in craft. ‘Twere good you let him know; For who, that’s but a queen, fair, sober, wise, Would from a paddock, from a bat, a gib, Such dear concernings hide? who would do so? No, in despite of sense and secrecy, Unpeg the basket on the house’s top, Let the birds fly, and, like the famous ape, To try conclusions, in the basket creep, And break your own neck down.

I must to England; you know that?

There’s letters sealed: and my two schoolfellows, Whom I will trust as I will adders fanged, They bear the mandate; they must sweep my way, And marshal me to knavery. Let it work; For ’tis the sport to have the enginer Hoist with his own petar: and’t shall go hard But I will delve one yard below their mines, And blow them at the moon: O, ’tis most sweet, When in one line two crafts directly meet. This man shall set me packing: I’ll lug the guts into the neighbour room. Mother, good night. Indeed this counsellor Is now most still, most secret and most grave, Who was in life a foolish prating knave. Come, sir, to draw toward an end with you. Good night, mother. Exeunt severally; Hamlet dragging in Polonius.


If it is true that we can never learn it “all”, then it is implied that is something else to learn. For this reason, I believe we as humans, are naturally insatiable.  Hungry for knowledge, for the things we do not yet know. While reading Shakespeare’s Hamlet, this feeling is no stranger to me.  They say that writers are “game players” and while reading Hamlet I feel as though Shakespeare was no exception. Every word used is a word meant to be used. Every reference, theme, character, etc. has a meaning.  So how does a person even being to comprehend the most remote nuances delivered to readers via Shakespeare himself…? Wordseer of course!

Okay, maybe that was a bit of an over-sell…but you get the point. Wordseer is a Digital Humanities tool designed to provide users with a deeper understanding of a text – in my case Shakespeare’s Hamlet.  Now I know what you’re thinking: how can a computer give me deeper insights than my own text and highlighter…? Well the main difference is your sanity; personally I would like to retain mine…for now. What do I mean? Well, the amount of time an individual would have to spend scanning a text in comparison to a computer is incredibly different – granted that the tool is working correctly…

By searching a single word in relation to a single character, hypothesis can be drawn. The fact that Wordseer exists to deliver these results to you makes the range of individual theories broaden. While experimenting on my own, it is interesting to note the evidence or even lack of evidence provided by the usage of words in a text so widely examined.  Interesting opinions of theme, character, and plot will creep into your mind, and then you will know…Wordseer has officially opened your eyes to any text you may be experimenting with.

Within Wordseer, lie a couple niftly tools to help users a little more visually. Perhaps I am interested to know how many times the word “death” is used in Hamlet in relation to the rest of the Shakespeare corpus…


With the first column representing Hamlet and each following column representing additional Shakespeare works, users can visually recognize the difference from play to play in regards to a single word usage.

But perhaps you are interested in looking a little more closely at one text particularly; easy enough.  This would be a great time to create a Word Tree. Word Trees are great for finding every instance of a single word in a text followed or preceded by the line(s) the word is used in.


This visual element can be helpful in determining the context in which a specific word may be used.

Overall, Wordseer is a great tool for users looking to dig a little deeper, while embracing a new method of analysis.  This tool can help you discover layers to text which may be easy to pass over, thus assisting in the formation of provocative
thesis and conclusions or even just some interesting thoughts! I hope you are ready to feel satiated, if only for a short while.


WordHoard: overcoming the adversity

Sweet are the uses of adversity, which like the toad, ugly and venomous, wears yet a precious jewel in his head.

How better to describe my experience of using Word Hoard to analyze Hamlet, then to use the words of Shakespeare himself? Although I, as well as my group members, faced some difficulties when trying to use WordHoard, the results were worth it.

One major grievance for myself was that every time I wanted to connect to WordHoard, I got the following message.

It wouldn’t be so bad if I could open the program after the second, third, maybe even the fourth time, but unfortunately I wasn’t that lucky. I did finally get to the database but only after I (1) uninstalled WordHoard, (2) downloaded it once again and (3) saw the above message two more times. By this point I wasn’t very happy with the WordHoard creators.

Once I finally connected to the database and chose my literary text and Act, I found that I was completely and utterly lost. Although I had attended the workshop on WordHoard and even read the “Getting Started” article, I had no idea where to start. Word Hoard has countless options when it comes to analyzing a text; so many that one would almost prefer having a program that’s limited but more straight forward and easy to manage.

My original objective was to analyze Hamlet’s anger towards his mother by finding a difference in his speech when they are alone or in public with others. My thoughts were that his true emotions would be revealed by comparing the words he uses to describe his mother in Act 3 to other Acts. Instead what happened is that I got sidetracked by the many other functions of WordHoard.

One of them happened to be the function where you can take a word, any word, and find out how many times it comes up in Hamlet as well as other Shakespeare plays. I found this very interesting as I tried to figure out WordHoard. Unfortunately the occurrence of ducat was insignificant to my objective.

I’m quite happy that I got WordHoard as the program that I get to work with because regardless of some of its difficulties and my wandering thoughts, I believe our group will get interesting results from our analysis. Once I better understand the majority of the functions in WordHoard it will be a lot easier to direct my analysis.

Getting Off on a Bad Foot

Admittedly, my first taste of Voyeur was tainted by it having been the only tool tutorial I had missed out on.  That having been said, I learned what I could from the video and web tutorials available online.  This was an immediate drawback to the tool for me as it all seemed very relative to previous text analysis tools and was presented it in somewhat of a bland fashion. In addition, the online tutorials created an image of an overly complex application of which the payout was not worth its difficulties.  In light of this, it seemed all too unfortunate that Voyeur, irony of ironies, was the tool assigned to me.
Post contract discussion and signing with fabulous Group D, I set about that very evening devoted to Voyeur and determined to unravel its bland mysteries…
As it turned out, Voyeur (formerly known as “Voyant”) has and continues to contribute to my more complete understanding of Hamlet.  Moreover, I was taken off-guard when I realized how entirely mistaken I was by labeling the program as “bland.”  As began to immerse myself into the aid and although it was a bumpy road in trying to understand how to achieve any analytical directives, I found myself enraptured with the endless possibilities of “word trends” and similar word frequency monitors and charts.  In the screenshot provided below, one can easily see how much you can read from the simplicity of searching the word “or.”  Squared off in red is the “segments” option where the user can select the amount of segments in which to stretch or squish the specific “revealed text,” in our case: Hamlet.  I have chosen 5 segments so as to better view my search results within the chart as Hamlet has 5 Acts, the math is pretty straight forward.
Additionally, squared off in blue in the same screenshot below, deeper exploration of the text is at the users fingertips as the “corpus reader” is open directly beside all of the companion exploration tools.  Aside from providing visualizations of the word frequencies, side blue bars of varying strengths guide you to the heaviest densities of your searched word.  Clicking on one of these bars (located to the left of the text) brings you directly to the specific segment in the play and highlights each searched word within the text.  Using the provided example “or,” in the blue square, a perfect example of the juxtaposition of the usage of the word.  Especially with the use of “or,” contrasting words like “heaven” and “hell” are set against each other and provide scrumptious brain-food for thought.  In my case, I was spurred on by this specific search and borrowed many of the opposing words I found and came up with some of my own, to discover what other secrets lie within the play.

When I met with Group D, we Voyeur’s shared our personal findings and experiences with the tool that we had discovered independently.  This added even more intrigue to Voyeur and its flexibility as  members of my group taught me additional pros, among them: it is completely customizable!  Aside from the website of origin, Voyeur has a site that allows users to blend their own skins depending on what you want to play around with or favoured gadgets (such as “bubblelines.”)  In the second screenshot, a simple breakdown of how this works is shown: just drag and drop!

As we’re all still experimenting with Voyeur, not all is uncovered yet.  However, as of yet the pros far out-weigh the cons.  Such cons being the bumpy road to discovery and some text visualizations rely heavily on java script: a highly fallible script reader, this shortcoming falling more so on Java and less on the program Voyant.
The experimentation has been more than entertaining with Voyeur, and as a result has already become my favourite tool, to my pleasant surprise.  Personally, I have a high preference for critical writing and analysis, and so the ability to broaden my own understanding of each play, act and/or scene is boundlessly amusing.  I look forward to discovering more independently and with my group.


Words and Their Relations: Wordseer and One of Its Uses

In English 203 I’ve been working with Wordseer as part of a group specializing in that tool. Because I’m new to the digital humanities field, I am also new to the tool Wordseer. In order to better understand Wordseer and how it helps me study the digital humanities, as well as to help along the other students in my class in it’s understanding, I came up with a couple of questions.

The first question I asked, and the one was “what is one use of Wordseer?”. What I found was that Wordseer is unique, among the tools in the Digital Humanities that I’m familiar with, in that it has a search function to find how words interact with each other. This is helpful in finding the opinions of characters towards certain things or other people. It is better to do this with specific people or places or things. Using Hamlet as the text, I entered Ophelia described as blank so as to find how the characters felt about or viewed Ophelia.

Ophelia described as blank

Ophelia described as blank

The results show that Ophelia was fair, poor and sweet. I can see this as a very useful and important tool because it gives me a good idea of how Shakespeare intended us to view Ophelia, as well as the overall opinion that the other characters have of her.

We can also go to the bottom of the page and look at the results in a better context.

This section of the tool is useful because it helps the user to understand the specific situations that the word is used in. The word is shown with a few lines around it, this allows the user to get the mood and the tone of the situation the word is used in. One problem, though, with this section is that it tends to be a limited view of the word, but, by clicking on the indicated icon, you can read the full section of the text that the word is used in and the text from the search page is highlighted to let the user know where the word is in the text.

This allows the user to know who is speaking, also allowing the user to know how that character feels about the word he or she searched for. In my case, from this search and only a few lines around the given sections of text surrounding each use of the word Ophelia I can find out these things about her in a very short amount of time:

  • She is fair of appearance.
  • She grows mad sometime during the play.
  • She drowns sometime during the play.
  • Hamlet in particular thinks her beautiful.
  • When she dies she is deeply missed by Laertes.

From this narrative, I’ve learned one excellent way to interpret a text with Wordseer. Using the search function, a user can interpret what a character place or thing is like. This is a very helpful function in literary analysis in that it can help define a character.

Trials and tribulations of Tapor: Teresa Vs Tapor

Going into this project I am filled with anxiety and a little bit of trepidation. Not only because it is a huge chunk of my final grade but also, because I am completely computer illiterate. Knowing that I have been taking out of my comfort zone made for some short nights and restless sleep.

TAPor is the program I was assigned to work with to analyze Hamlet Act 3.4. As I am the type of person whom needs the most user friendly program to work with, and Tapor is definitely not one of them. My initial goal was to use the program to figure out if Hamlet can see the ghost of King Hamlet or, if in Act 3.4, he had a psychotic break and is on a downward spiral. My initial goal was quickly switched to, “ how to use this program?”

My group and I had sat down to get acquitted with TAPor and play around to see how it all worked. I am sure TAPor could smell my fear because while everyone in my group were getting results, all I kept getting was error messages. It was funny at first until I did not have a positive result during this stage, then humor turned into frustration. The main thing I did realize during this play around stage is that TAPor has a ridiculous assortment of error messages and it is very rare to get the same one more then once. I banged my head against the desk and wished that the developers put more time into making their program user friendly, then devising a wide array of error messages.

I am not one to give up so I took a day’s break to clear my head and start fresh. Thinking that if I were not afraid of it, it would play nice and give me results. Boy, was I ever wrong. I continued with my original question of, is Hamlet actually crazy in the scene with a small hope that a days break TAPor will work. I enlisted my fellow group members to help me isolate words and themes to find patterns and yet again, all I seem to get was an error messages. From the dozens I have received I have compiled a few of my favorites that are worth noting.


With my lack of computer skills crushed and my large part of my final grade on the line I refuse to cower to a computer program. I stopped trying to isolate certain words to help me figure out my problem and started to see if any of the many tools would work. After what seems like days, TAPor cooperated with me! It gave me a list of words that have capital letters.


How will this help me, or anyone else, trying to analyze Shakespeare? I do not know, but it was a start on the right path. This was a small victory against a computer program that wants to make me sweat about a subjected that I have always loved and enjoyed.

Once I got my list of capitalized words, I could not help but notice that, while some people would think the interface was dull and lacking color, I liked the simplicity. It may have only been my reaction after days of struggling to get the program to work or if I genuinely liked the look. It is definitely way too early for me to decide at this point.  But as I continue the daunting task of working with/against TAPor, I hope my progress improves and that I can proudly say I have mastered TAPor by the end of term.

Family Affairs in the Digital Humanities

To begin writing blog posts I was extremely nervous. What if I wrote something unintelligent? First of all, the whole world would have access to it, and the thought that the whole class and not just the professor would read it really had me sweating! It doesn’t help that I’m completely technologically challenged either, and prefer to do things the old fashioned way such as writing my notes with a pen and paper (gasp!). But despite this, one thing I realized as I tried to be enthusiastic about this whole other world to me was that it makes things so much easier! Less time, looks cleaner and more polished, and way more people can see other things that you post even if it’s not school related (scary at first, but kind of cool now). Imagine getting my stuff published and recognized by a much larger audience…this would be the way to do it! The only problem about this supposedly easier method is that if you don’t know how to do squat on the computer, its way harder before it gets any easier.

So my goal was to get used to using this method, so that soon enough I could do this with my eyes closed (so I hope at least). It drove me absolutely crazy when my browser wouldn’t load, or that my laptop came up with blank pages. Why wasn’t this working as smoothly as I hoped? The funny thing is, this was no longer an individual thing I did on my own (besides my group members), it became a family affair. The amount of times I got my poor father to fix the wi-fi, pleaded with him to call SHAW, and even after all that the browser was still slow… I cried saying that my laptop is crap and that this was his fault because he made me get a PC when I insisted I wanted a Mac.

Funny business aside,

After watching the video tutorials on WordSeer and trying as many different things as I could, I discovered some really cool things. Obviously, I had learnt about some of the benefits of WordSeer from the class workshop, but it was different when I started to play with it on my own. It just “clicked” and finally the light bulb lit up. Unfortunately this didn’t happen before I started the whole incident with my father…sorry dad!

The good news is, after my dad sat me down and made me explain the whole point of the digital humanities and why this mattered so much (I asked the same thing at the beginning of the semester), he seemed really impressed! My dad lives on his computer and does all that hard math excel stuff. He didn’t know that an English major could use so much technology to further enhance her “field.” So all in all, it started off bad and frustrating, but turned out to be really valuable and my dad gave me the “nod” of approval! Note to self- dad associates technology with importance, good to know. And I no longer have to be associated as that child who’s useless because she isn’t becoming a dentist.

I must admit that I was really relieved that I was doing WordSeer. It seemed like the least complicated next to Wordhoard during the workshops, and after playing around with it  (when the browser was working) I realized how creative and so easy to use it was. I was impressed by the high quality of imagery and being a visual person it was easier to comprehend. I am still on the process of working on how to do snippets, but the gist of the program such as making collections and seeing the comparisons with Shakespeare’s other works to Hamlet makes it easier to identify the themes and significance of certain words. I liked the fact that with WordSeer the results can be as simple or as complicated as you make it. For example, you are able to just get words from one scene of one play, or compare words from all the plays from a certain genre such as tragedy, plu more! This is a great tool for initial research and after doing the writing skills exercise in class, it dawned on me that this is an awesome place to start studying neologisms and etymologies of each word (an assignment I had to do for my Shakespeare’s class).

As for specific findings on Hamlet act 3, scene 4- I haven’t gone too much into this as I was spending much of my time navigating through the program. However, I am quite excited to discover whatever WordSeer will offer me now that I have some confidence in using the program (and the computer). In my next blog post I plan to focus on my findings from this scene and elaborate more specifically on all the benefits of WordSeer.

MONK’s “pranks…too broad to bear with”

Polonius’ sentiments about Hamlet’s ‘recent behaviour’ were perhaps approached in our MONK group today.

Being met with frustration on the first day of collective contribution to learning and mastering MONK, I believe, though my teammates may disagree, was both beneficial and disconcerting. MONK, amongst other capabilities (albeit extremely limited capabilities), immediately bonded us in the united effort to overcome its barricades of text analysis. A united effort that made a modest amount of progress, but progress nevertheless. Our processes, and the obstacles that MONK hurled our way, as depicted and described below, have revealed to us the limitations of MONK’s capabilities.

To begin, I depart with my emphasis on the limitations of MONK’s capabilities, to explain what those capabilities are. Then the limitations which are to follow will be of much more significance and clarity. In a general overview, MONK is an acronym for “Metadata Offer New Knowledge.” It functions on a ‘bag of words model’ in which it takes a digital text and interprets the characters in the entire text as numerical values. The ‘bags of words’ (called worksets from here), are compared with other kinds of bags in order to provide a frequency comparison with other texts. It is an analytic tool, where we enter data so that the tool can give data back. Thus, in summary, MONK is able to search concordances such as lemmas, parts of speech, and spelling, which are all inputs for Dunnings. It is also able to compare the frequency of any of these three between two worksets through the use of toolsets. Those who are interested in further details, or feel that my explanation leaves much to be desired, may proceed to the Monk Tutorial. For those who are interested in Dunnings, and the analytics of it, may proceed here.

We defined our worksets as chunks of text, instead of as lemmas, parts of speech or spelling, as to suit our purposes of analyzing Hamlet 3.4. The worksets that I am currently attempting to work with are the complete text of Hamlet, Act III, and Act III scene iv.

We began our first session by exploring our tool in an attempt to grasp it’s full potential in analytic capabilities. Though not verbally stated, I imagine the question we sought to answer was, ‘what can MONK do to provide me with more insight than what I could get from simply reading the text?’ With this general aim in mind, we started by searching general concordances in Hamlet just to practice using it. We entered, in the concordance search bar, “mother n” in order to search for the frequency at which mother appears throughout the text as a noun:

As you would guess, “mother” as a noun, does not appear this many times in sequences throughout Hamlet. The problem presented here that we continued to experience, was that the findings do not provide us with any line numbers or references to acts. We are left with the general picture of how many times we see the word “mother.”

Regardless, we continued on to see if perhaps the toolset “compare worksets” would provide us more insight into the significance of frequencies in Dunnings as opposed to concordance of just an isolated text. So, upon saving our worksets, we entered into the tool and before starting to even use the tool, we were already faced with another problem: what could we compare Hamlet 3.4 for with in order to obtain useful results?

Because MONK is a comparison tool, we determined the best ways in which we could establish the significance of Hamlet 3.4 to Hamlet in general, was to compare 3.4 to the entire text of Hamlet, 3.4 to Act 3 (excluding 3.4). At this point took our own experimental paths, continuing to share with one another what we found, what problems we experienced, and questioning what we could do to take that result to further analysis. The following is what I found in my own attempts to use MONK. (However, the problems that are described here are ones that all five of us encountered.)


First, the feature comparison has several analysis methods available in the drop menu:

On The left hand side of the screen, I have set the first work set as Act 3.4 and the full Hamlet text as the second. The ‘Analysis Methods’ drop menu contains the options “Dunnings: First workset as analysis; Dunnings: Second work set as analysis; and Frequency Comparison.” The remaining two I have yet to venture into.


The results on the right were the result of selecting “Dunnings: First work set as analysis” and then selecting ‘Lemma’ as a feature, 30 as the minimum frequency, and ‘nouns’ for feature class. These data inputs returned to me the data results on the right, in which the left hand column displays numerical values of the frequencies, and the right displays a visual guide in which grey words are under used, and black overused. The size of the font used reflects the extent of over or under use; the bigger the grey text, the greater the under use and vice versa.

This is where my problems began. To stop myself from rambling, I will just mention in brief that the problems that I experienced in comparing 3.4 to Act 3 workset were the same, if not worse.

In comparing 3.4 to Hamlet as a whole, whether altering the analysis method, changing the minimum frequency, or switching from lemma to spelling in the feature drop menu, there were very little changes that could be noticed in the frequencies on the right hand side.

For example:

This was the result of  the following parameters:

  •     First Workset- Hamlet 3.4
  • Second Workset- Hamlet (full)
  • Analysis Method: Dunnings: First workset as analysis
  • Minimum Frequency: 20.
  • Feature: Lemma

**Please note the bold grey letters, as the list reflects those letters



The parameters set for this second analysis:

  • First Workset: Hamlet 3.4
  • Second Workset: Hamlet (full)
  • Analysis Method: Dunnings: Second workset as analysis
  • Minimum Frequency: 20
  • Feature: Lemma

As you can see, the words are exactly the same, whether you are using the first or second workset as analysis. I assure you, the results are equally baffling. The logic behind our thinking here, was that 3.4 as a significantly smaller body of text, would return different results whether it was the text being analyzed, or the text being compared.

This was just one example of the various parameters I manipulated in order to generate results. This was a problem that we all experienced as a group. In an attempt to determine if we were missing something or otherwise incorrect, we used the same tool to compare Hamlet to the genre of tragedies available in the MONK database. The results varied greatly with this search.


This is what we realized:

MONK is capable of establishing very interesting data on the frequencies of words and lemmas within texts, but only if it is a large and substantial amount of text. This comparison technique is useful for the comparison of genre to genre, as it looks to the general significances of frequencies. However, the frequencies that exist within one scene, one act, or even one play, are difficult to use in establishing an argument. MONK is designed to be used in the broad spectrum of language that Shakespeare employs.

Because of this, when trying to analyze smaller bodies of texts, results became increasingly harder to establish as significant.

In the MONK tutorial, the section titled “Basic Facts on Common and Rare Words” explains the concept of Zipf’s Law, and explains that the words that occur rarest are the ones that will be the most interesting and significant, as opposed to the more common ones.

This being the case, it has been difficult (as of now) for us to look past the limitations and difficulties of MONK and embrace the potential it may have, as the frequency of words in 3.4 compared to Hamlet as a whole, is bound to be among all the rare due to the difference in content.

Nevertheless, as Hamlet says, “There is nothing either good or bad, but thinking makes it so.”

I believe our next step is to question: “In what ways can we manipulate MONK in order to use it in innovative ways in order to draw insight from dunning frequencies and workset comparisons to study Hamlet 3.4?”

Perhaps there are some ideas here.

Innovation: that’s what the Digital Humanities is all about right?


The Greatest Limitation to WordHoard is its User

This has been quite an adventure- figuring out wordhoard and  blog posting. Firstly, I had the worst time ever getting to post this. I couldn’t find the blog once I’d signed up for it. Thankfully I did find it. So I was trying to use wordhoard and thinking about how Hamlet’s mood changes in act 3 scene 4, and decided I would use that question to try to figure out wordhoard and how it really works. That would’ve been great, had wordhoard loaded right away. It did not. I was stuck looking at:


For about half an hour. Quite frustrating. Once wordhoard did load, I went ahead searching my lemmas “love”, “brother”, “husband”, “mother”. I hoped that if I could find these and how often they were said, I could then narrow my search down into who says which words as best as can be done ( “man”/ “woman”/ “immortal”) and then continue on my merry way. Except I couldn’t figure out how to limit it to just Hamlet. I manged to open the Hamlet document from the table of contents, but I couldn’t get much farther than that. So far I have established that it is probably me and my computer ineptitude that is limiting wordhoard, not wordhoard limiting my searches. Once I had my searches typed in and had managed to limit everything to only Shakekspeare, I got this page:

This is about as far as I’ve got in searching with Wordhoard. I could not click on anything. Or rather, I could click on it, but nothing happened. So I couldn’t open up the search results to see where the findings were. I did not realize until then just how bad I am with technological tools. I was fine with it in the workshop, remembered it as being easy, and have failed miserably. I also couldn’t further limit by gender speaker and have that open with different lemmas or words and am seriously questioning if part of my failing with this is my computer itself. In any case, it is way to close to the deadline of this post for me to fight with wordhoard and/ or my computer tonight and come up with a brilliant blog post about my brilliant success. Needless to say, I will be spending all day tomorrow locked in epic battle with wordhoard to figure it out. I promise a much better blog post once I have slayed the beast (fellow group A members, I will be severely bothering you tomorrow if I cannot figure anything out). As of yet, I can see that the possibilities of using wordhoard are fantastic- what with all the drop down menus to select things and narrow down the search questions. As for the limitations, I am going to conclude that the biggest one will be myself (I believe I have been doing something drastically wrong to have gotten such little results), which is not an element belonging to wordhoard exclusively, but will pose a problem. I thought wordhoard was the most straightforward, simple tool of them all. How wrong I have been. Either that, or I would have been floundering even more helplessly had I been in a different group. I apologize for being computer challenged. However, I did successfully figure out how to take screen shots and upload them into the blog with minimal suffering. I give myself kudos for this.

Thoughts on TAPoR analysis of Hamlet 3.4

The most noticeable thing about TAPoR is its seemingly infinite amount of unique error messages, and lack of user-friendly design. When analyzing a text, if I use more than one tool per session, the error message: “Sorry, you are trying to access a private text. Please login or contact the owner of the text for permission” is shown. TAPoR also helpfully supplies an analysis of the text within this error message with whatever tool I was trying to use.

Besides this interesting quirk, another issue is using TAPoR on such a small part of Hamlet (Act 3 scene 4) as opposed to the full text. This renders the most visual tool, Fixed Phrase, useless. “Visual”, on a side note, is a very generous word to describe this tool. Below is an example of using Fixed Phrase to search the word “look” through all of Hamlet vs just act 3 scene 4.

 One positive use of TAPoR is the CAPs finder tool – a tool that finds all capital letters, excluding those at the beginning of the sentence. It allows you to easily find the allusions made to Mercury, Jove, and Mars (3.4.57-59).  It is not without its flaws though. Due to the tool excluding the beginning of sentences, it misses the allusion to “Hyperion’s curl” made in line 57.

 The most useful tool on a small space of analysis is the List Words tool. This tool, when sorted from highest frequency to lowest, shows the most common words found within 3.4. Excluding character names, the most common words are “thou”, “look”, and “good”. What is most intriguing about this list is the distribution of the world “good” within 3.4. Of the ten times it is said – and mostly by Hamlet – it is almost entirely after the ghost has come and gone.

You’ll notice that the distribution graph mysteriously ends after the first 5 words. It is the same when analyzing the full text, and unfortunately the word “good”, although said a lot throughout Hamlet, does not get a distribution graph. This makes any comparisons between words in 3.4 and the rest of the text a little discouraging.

From conversations within the TAPoR group, we decided the two most important themes for this scene are Hamlet’s madness, and his relationship with his mother. The distribution of the word “good” – being that it mostly occurs after the ghost advises Hamlet to calm his mother – along with the fact that it is Hamlet saying the word to his mother 9/10 times, hints that there is something to gain from this analysis for both themes. The theme I feel it most strongly says something about is Hamlet’s madness, or his false madness.  Whether or not the word is used as a simple pleasantry (as it often is with “good night”), doesn’t affect the importance of this analysis. His repetition of the word after the ghost’s appearance suggests that he is either trying to convince his mother, himself, or both that all is “good”.  My first tentative conclusion is that Hamlet is questioning his own sanity due to the fact that his mother was unable to see or hear the apparition he believed was in front of both of them. His reaction is to hastily bumble several “good nights”, as well as several other mentions of being “good” and calmly drag Polonius’ corpse from the room as if nothing is wrong.

More quantitative research will be needed to confidently assert this. Searches, frequency, and distribution of synonyms (such as “fair”, “well” or “fine”) could help prove or disprove this conclusion. Close reading, and more qualitative analysis outside of TAPoR (before trying to work with this information within TAPoR) will help form my next post.



Monk; the bad and the beautiful.

My initial reaction of the text analysis program “Monk” was that I figured compared to the others, it seemed to have a fairly modern feel and look to it. I instantly had high hopes that it would be the most up to date program we had examined out of the five. Unfortunately, my optimistic approach didn’t last as long as I would have liked. I desperetely spent the entire TDFL session trying to log in to the website but had no such luck. Thus began the hell we now call, Monk.
Besides discussing the troubles and frustrations we had all individually encountered, our group did manage to sort out most of the kinks of the program. That being said, there were a lot of glitches discovered in the program as well as an overall sense of confusion. It seems as though the designers/creators of Monk decided it was necessary to make a maze of disaster to get to the final outcome of what you were looking for. To put it simply, nothing comes easy in regards to Monk.
Getting back to the introductory problem, Monk has its fair set of problems when it comes to logging in, in general. If you try to log on to Monk while in internet explorer, it won’t work. Simple as that. A fellow group member suggested I try using google chrome or firefox and only then did it complete the login process. My big question here… why?! I can’t even begin to understand why Monk chooses not to run while in the most basic internet option. Frustrating doesn’t even begin to describe how I was feeling.
Not only does Monk choose to be difficult when it comes to log in in, there is also no save option anywhere on the site although it claims that there is one. This means you have to basically start from scratch every time you want to begin your research on specific lemmas or anything else you have done for that matter.
There are also numerous annoying glitches such as it telling me that I haven’t clicked a workset to work on but I so clearly have. You just have to wrestle with it for a bit before it finally decides to accept the fact that you truly indeed have chosen the workset.

It became very clear that Monk is mostly designated for comparisons. While it allows you to create a workset (a document including texts that you want to analyze), this really only comes in use when you are comparing two texts. If you are looking to examine one piece, for example Act 3 Scene 4, it allows you to click the workset you have previously uploaded and saved, but after choosing it makes you do it all over again. It truly makes no sense, and is a continuing hassle to constantly have to re-choose what you are looking to analyze. The worst part is that it makes you think you won’t have to, but you do! They could at least acknowledge the present problems rather than act as if they don’t exist.
Despite the havoc we encountered while trying to sort things out with Monk, we did manage to get some good ideas rolling for what we want to specifically look at and what we can achieve and discover through the program. Despite the general hatred we feel for Monk, I still feel positive that we will be able to make it work…somehow.

My experiences of what TAPoR can do, and what it can’t

TAPoR, although filled with gadgets and gizmos, is unfortunately, not very user friendly. Trying to work with the different tools, just to get used to the system and the layout of the program, was quite an adventure. With all the tools available, it is very intimidating trying to find data. That, mixed with the fact that this system is extremely temperamental, and that only certain tools work with the format, makes for a very difficult investigation. Through multiple attempts, and almost just as many failures, I learned that the only tools that will not come up with some sort of “error” message, are tools that end with (html). As well as that, I cannot seem to use my saved texts in the tools, and I have to resort to using a url. This is fine, except for the fact that some words that are not included in Hamlet are included when using these tools. For example, when using the tool TAPoRware List Words(html), words that Shakespeare definitely did not use, such as “email” and “cite” ,were included in the results. Another issue with this specific tool is that only the first five most common words used were shown what their distribution was. This is really quite pointless, considering the first two words are the names of the speakers, and the others are common words that don’t give us very much insight into the text. It would be a lot more useful to show the distribution of all of the commonly used words so that you can compare when they are used.
Besides the fact that this program has many issues and an arsenal of error messages, it is really not that exciting. After using different TAPoR tools for a long length of time, I find myself having to go to the bottom of the tools, and use the word cloud from Voyeur just to add some color to my life. As well as aesthetically pleasing, the word cloud tells me all of the information the List Words does, minus the dispersion of the first five words. Why can’t an actual TAPoR program be like this? Instead of listing words in monochrome colors and boring fonts, they should maybe try to focus on making the program a bit more fun to use. After they fix the program so that an error message doesn’t pop up every second attempt, of source. That should be their main priority.
Although the system has its flaws, and it lacks excitement, there is one thing I do really like about TAPoR. I think it was a really good idea of the creators to exlcude all of the stop words in their tools. The TAPoR tools do this automatically, and it does make it easier to find themes and connections without all of the words that we really don’t need to look at. With the Voyeur Cloud, words like “and” and “the” show up as the most used words, which we really don’t care about. You can change the format of this tool around so that it does exclude all of the stop words, but without help finding it, I would have never known that option was available.Hopefully, next week will be more about the text, and less about the program.

Monk Workbench: Either the most simple or the most complicated tool in the Digital Humanities.

Kelsey Judd, First post.

Today was our first group confrontation of the program MONK.
It began well, with each member contributing what they had learned over the last week, and with all of us piecing together our separate knowledge to unravel the mysteries of the work tools. Within an hour we had discovered all the ins and outs of the program’s most useful components which I will try to explain: “Define Worksets” for finding concordances in lemmas or spelling, and “Compare” for finding frequency and Dunning’s analysis. Unfortunately soon after this we hit something like an impassible brick wall. Either due to out lack of experience or to something we cannot quite figure out in the program there does not seem to be all that much more to it beyond “Define” and “Compare,”…

The define feature is fairly straight forward once you realize one main point: it does not seem to keep an actual record of your “worksets.”

You can choose a tool on its own, or add a workset to work with.

We found that when you choose the “define worksets” tool it does not affect a tool if you choose a workset to go with it. Either way you come up with this page

It goes here whether you have a workset selected or not.

From here there are only two options. You can create a workset, which is basically searching Shakespeare’s works, or various works of American fiction and then saving your search and naming it. The second option is to search for lemmas, spelling or parts of speech; however, this does not seem to do anything. Whenever we try it, it will still ask you for which work you are searching in, even if you defined Hamlet or act 3.4 as your workset on the main work page or within the tool previously.

From this page, when you have selected Act 3, scene 4 comes a very simple little tool where you can search concordance. All you need is for the text to appear in the “advanced viewer” and to of course search on the concordance tab below it. Simple and straightforward. The only problem with this was that while it tells you all of the words or lemmas in which the word appears, and tells you how often they appear, it does not provide the speaker or location of the line, so it is mostly up to context. Now, I am sure there must be more to use in the define/edit worksets tools, but for some reason the five of us could not find it. Sounds like we still have a lot of exploring to do.

The other very useful tool is the “compare worksets” tool. It allows you to pull up specific texts, for example Hamlet as a whole, compared to just Act 3, scene 4.
It allows you to see the frequency or do a Dunning’s analysis of a word or a lemma, with the two variables being Shakespeare’s other works or works within a text. We found this works much better when used on a larger scale, such as comparing Hamlet to another play, or the whole of Shakespeare’s works.

Beware: the words on the far right run together sometimes, so you end up getting excited on finding the new word "actairbed."

As you can see the strange feature of this is that the words sometimes run together, so you think you have found a cool word: “actairbed,” when it’s really just the three close together. Amateur mistake of course. Clicking on the words will take you back to the spelling search and you will once again see the context and frequency with which they are used. The frequencies are quite a neat discovery, I think one of our next projects will be on how to use this tool to discover new and exciting themes in Hamlet act 3, scene 4.

End of the line?

Overall the experience with MONK has been a lot of trial and error, but rewarding when we do manage to find something new. The biggest problem we are having is the feeling that we are missing something crucial; we just seem to be going in circles. After upwards of three hours it may not seem like a lot, but has been quite a journey despite the time. Of course we will be pretty excited when we can successfully report back about new findings, most of all when we figure out how to save results… but for now figuring out the concordance and frequency tools has been rewarding.

Monk: Why Can’t We All Just Get Along?

As I left room 440a of the Taylor Family Digital Library after the in-class Monk workshop, I was pleased that I had had been assigned to work with this tool.  Initially out of all the tools I found Monk to be the most visually appealing, based on it’s simple layout and lack of clutter.  During the workshop I learned that I was able to compare Shakespeare’s comedies and tragedies, and mored specifically identify the nouns used in both plays.

This toolset, entitled Compare Workset, seemed to have a lot of potential and I was looking forward to applying it to Hamlet and seeing what new information I could learn about this play. As I began to work with Monk on my own time, I discovered that there were many other tools just waiting to be used, and I was eager to try them out.

And now, two weeks later, I find myself at a standstill.

Monk provides default toolsets, which can be used to analyze a workset.  A workset is a text, however big or small that the user has saved to his/her project.  My team mates and I created two work sets, the first being the entire play of Hamlet.  The second being Act 3 Scene 4 of Hamlet.  We wanted to use the toolsets to study each workset individually and compare them to each other, and see what results we would get.  I personally had an immediate interest in taking advantage in all the extra toolsets that I could add to my own project.  Toolsets such as Text Viewer, Analysis Tool, and Knot seemed to be helpful and relevant tools to my project so I tried them out.  When I tried to use Knot I got this:

When I tried to use Analysis Tool, I got this:

When I tried to use the Text Viewer, I got this:

I had barely even started using Monk, and already it was limiting me.  How was I supposed to get the most out of using Monk if it won’t even let me have access to all the tools that it offers?  As I made more and more attempts to work with these tools I became more and more frustrated.  A more detailed explanation of my groups attempts and aggravations can be seen in my team mate Hayley Dunmire’s blog:  I want to focus one of the limitations that my team mates and I discovered.  The toolset Compare Workset allows me to compare my workset of Act 3 Scene 4 to my workset of Hamlet.  I selected the Analysis Method Dunnings: First workset as analysis set, filled out the rest of the requirements and clicked compare.  I got a listing of words from most commonly to rarely used.  By clicking on one of the most commonly used words, I was brought to a page that listed all the times death was used in Hamlet.

I guess that’s interesting.  But what am I supposed to do with that?  I’m able to use the toolset Classification to isolate the use of death in Act 3 Scene 4 to just one time, but that’s all.  The more times I’m using Monk the more I was starting to realize that on smaller scale, it’s difficult to make any major progress in discovering anything of significance.  My team mates and I bounced around ideas such as looking at the type of words Hamlet uses to speak to Gertrude in this scene as compared to the rest of Hamlet.  But the results (as seen above) do not show the speaker or line number.

As angry as I am at Monk and how much it’s limiting me, I don’t want to give up on it.  My group and I came to the conclusion in our meeting today that Monk seems to be a tool that is better for large scale comparisons as opposed to small scale analysis.  For  Phase 1 this puts us in a tight position.  But I’m planning on working with Monk (yes with, not against) over the weekend hoping to come up with new ideas on how we can work with the toolsets that Monk has to offer, instead of trying to fit the toolsets into what we think they can provide.


Momentary Panic and Gradual Acceptance

Before starting this group project I was extremely hesitant about using digital tools to explore texts. Walking into class the first day, I was unaware of the digital humanities aspect of the course. I thought that it would be another run-of-the-mill english course complete with essays and when the digital aspect was introduced I will admit to being slightly taken aback. I was so used to the cookie-cutter high school approach to learning english, (read book, discuss book, write essay on book repeat) that this new approach to learning slightly scared me. Add two group projects and a twitter assignment and I am slightly ashamed to say that I became very dubious about the whole experience. However, through exploring my assigned tool (Voyeur) I am beginning to recognize the newfound advantages of the digital humanities and how we can examine literature through them.

Voyeur is an extremely nuanced tool with many different features for examining text. One of its strongest features is the visual element that it incorporates into nearly every tool that it offers. I find that the visual aspect offered to me by Voyeur allows me to experience text in a way that I otherwise would not have. Initially exploring Voyeur on my own the tool that caught my attention was the Word Trends tool. It divides the corpus you upload into sections and creates a graph showing either the relative or raw frequencies of words throughout the corpus. For example, (within Act 3 Scene 4) I looked at the relative frequencies of the terms “king” and “Hamlet” (When spoken by a character) and told Voyeur to divide the corpus into 10 sections. I achieved this graph…

Through this graph we can see when the subjects of “Kings” and Hamlet were discussed in Act 3 Scene 4, where they intersect and the rise and fall of these ideas throughout the scene. Through this tool I began exploring the frequency of other words throughout the scene and how the frequency of those words reflected the progression of themes and dialogue in Hamlet. This new graphical way of understanding text greatly appealed to me. In looking for other connections between words/themes I soon became frustrated with the XML file we had been provided. Voyeur takes the corpus as a whole and does not distinguish between Hamlet when referenced as a speaker and Hamlet when referenced by a character in the play. I created my own version of the text by copying and pasting the text from another source, making sure the text fit the version of the play we were given and giving “code names” to each speaker. To properly distinguish between Hamlet when he was speaking and Hamlet as referenced by another character, I gave him the code name Hmlt when he appeared as a speaker and repeated the process for Gertrude (Grte), Polonius (Plns) and the Ghost (Ghst)…

Using this system, not only was I able to separate speakers from speech, I was also able to track the frequency of each speaker though out the scene using the code names. For example, here are the relative speaker frequencies of Hamlet and Gertrude throughout the scene…


Those results are not incredibly surprising considering that Gertrude and Hamlet and the two main speakers within this scene however, it illustrates the flexibility of Voyeur and its ability to examine more then one facet of text. My group also discovered that Voyeur has a customizable layout where you can include different tools that are helpful to what you want to examine and exclude tools that are not quite as helpful. Overall I am beginning to warm up to this new style of examining text and hope to continue to discover more about Voyeur in the coming week.



Minor Inconveniences with WordHoard

After attending tutorials on how to use all of the tools I’m very thankful I ended up with WordHoard. It has Hamlet loaded into it already and the rest of Shakespeare’s works, as well as works from Spenser, Chaucer, and Early Greek Epics. It allows me to (somewhat) easily look up:

  • Which parts of the text are narration and which parts are speech
  • Whose speaking
  • Identify the difference between male and female voices
  • Speaker mortality (I think it’s really cool for a program to single out supernatural figures from moral figures, perfect for Greek Epics)
  • If it’s written in prose or verse
  • Lemmas (which I had to look up what this meant…)

Other than that everything is a whole lot more complicated.

Continue reading

Monk- My Frustration and Lack of Findings…

My findings with Monk have been one of a love hate relationship. At the beginning with the in class workshop I thought that my tool and I would have a pleasant bond but, I was wrong. I have found that with Monk it sets you up with the basic steps and how to analyze things on a very broad spectrum through the comparison of texts. However if you wish to dig deeper an analyze through smaller parts of plays then you are limited.

The workset itself is very easy and welcoming, it makes it easy to find works, pick them apart and make them your own by modifying them in whatever way you wish. In our group each one of us a workset that is everything in Hamlet but 3.4. This makes it interesting to see what is there and what isn’t when you take away a section of the text. I like how you can go back and easily tweak and re-work worksets and they are easily available for ready use.


However you can only use the worksets that you have created to combine with one another for compare worksets and combine worksets.  Classification only allows you to use one workset at a time which I find very frustrating because I have to write down my findings then switch over to the other workset, do the same thing then manually compare the two together. I also found with my group that if you wish to compare worksets on a smaller level it doesn’t make much sense. We found that when we were looking at the whole play compared with 3.4 the analysis from the compare worksets from looking at both did not seem to make sense and the data would be relatively the same.


I found that the concordance was helpful with picking out certain words in an body of works or a scene however it does not tell you who the speaker is or where it was found. This is also frustrating because if I would like to have a further grasp of the scene and what it means I have to go through the play and find out where the word is mentioned to see who said it and in what context it was said. This makes the concordance useless if I have to go through and look for the specific word I can easily find it on my own. From this I can see that it’s only practical job is to count words.

You would think that Monk would be willing to help you along the way with all the question buttons available to look, however those are utterly useless itself.

I found myself and my group looking on Wikipedia and Google in search of the answers you think the program would offer you, but even that came up short.

I was thrilled to know that Monk had a bunch of fancy work set tools that you can play and make your own into whatever you wish. Unfortunately I have found that they do not work at all, I have tried countless times logging on and off, restarting my computer and switching the file of comparison but nothing seems to make these files work.  It is sad because it would be so neat to see the findings that you get on a different view point or aspect.  It does seem to be another thing Monk fails to do.

I would like to try to see if I am able to upload  entire works said by certain characters or pivotal conversations to compare to Hamlet or Shakespeare’s Tragedies.  I think that this would be very helpful in looking deeper into the text. However this seems like a lot of work to conform to the regulations made by Monk when other tools do this for you.

Overall Monk is handy if you would like to know the word count of a certain body of text or the comparison of two things on a much larger scale. From my point of view it is not made to dissect texts on a small scale but rather a very large one when looking at massive bodies of work. Anything that can bring you details seems to not match up or make sense when you try to pick it apart, and that makes me unsure of any information that I may be receiving from it.  To me Monk is like the tool that sets you up for the much larger tools, it seems to show you the basics and stop there. It then becomes up to you to work with other tools to conform to Monk. That seems a bit unfair since I think the tool should have this option already. I think Monk has failed to meet its standards that it presents and it is all flash and no substance to it, it has left me confused, frustrated and with more questions than answers.

Unlocking the Mystery That Is WordHoard

From my experience with learning how to use all of these digital humanities tools in Ullyot’s workshops, I found WordHoard to be one of the most straight-forward options. It has a simple interface and no extra flashy features.  While trying to come up with some sort of clever anecdote to start this blog post off with, I decided to take myself back to the actual WordHoard website to find more information about the tool.  One thing the site mentioned was what “WordHoard” actually means.  It turns out that the tool is named after an Old English phrase for “unlocked”.  I thought this was an extremely fitting name for the tool seeing as it almost feels like an intricate maze that needs the correct key to “unlock” answers in order to use it effectively.  Knowing how to correctly submit queries is like the “key” to the treasure.   Without the right knowledge of how to operate the tool, WordHoard can seem like a mysterious abyss filled with unreachable answers.

If you have a specific idea for something you want to find, Word Hoard allows you to fill in all of the criteria and run a search through any body of Shakespeare’s work (or the work of Chaucer, Spenser, and Early Greek Epic) to find an answer. This is a great asset to the tool because you have every text in its entirety right there in front of you to use if you need, without having to import any texts of your own.  All of the textual data stored in WordHoard is deeply tagged, allowing for people to explore their queries thoroughly. But the searches unfortunately don’t always come up with good results, and sometimes you end up with no results at all. You have to play around with the criteria until you can find something close to what you were looking for, and this can be limiting for the user if they cannot figure out how to properly enter their query.   The annoying thing about fiddling around with the query is that you have to restart every single time; you can’t just edit one part of it. For example, I tried to search for the amount of times Hamlet spoke about “love” in Act 3 Scene 4.  I wanted to see the amount of times he used it as a noun versus a verb.  So I entered the first query to look like this, selected “noun” first:

But my original search window disappears as soon as I click the “Find” button to give me the results, pictured below:

So in order to go back and see how many times Hamlet spoke of love as a verb in Act 3 Scene 4, I’d have to fill out the entire query again but this time selecting “verb”.  This tends to be very inconvenient if you’re trying to find answers quickly.

The interface of WordHoard includes a lot of drop down menus, which can lead you to exactly what you are looking for in a text query.  The one issue I find with the drop down menus is that there are just too many of them.  If I didn’t click on a certain menu, then I wouldn’t be led to numerous other options branching off of that one.  This is where the “mysterious abyss” description comes into play.  There are just so many ways to submit a query on WordHoard that it is difficult to know which ones to use and how to find them amongst the other options.  See the image below for an example of the numerous options WordHoard offers.  One can continuously click the “+/-“ buttons on the left hand side of the window and bring up more and more options, all of which have their own drop down menus to select from.  This can be very overwhelming for users to grasp if they are not already knowledgeable with the tool.

As you can see in the image above, the “Find Words” function allows you to submit a query on any word in Shakespeare’s texts.  You can select everything from the lemma down to the parts of speech, spelling, major word class, which specific work, the part of a specific work, author, publication year, narration or speech, speaker, speaker gender, prose or verse, or speaker mortality.

All in all, WordHoard has a lot of potential to be a very useful and effective tool when studying something such as Hamlet.  This important thing to remember about the tool is that you need to have a really good feel for its numerous menus and options so that you can effectively find the best answers possible for the queries you submit.  Otherwise, you will be wasting time restarting your query every time you want to fix one part of your search, which could prove to be a little frustrating!  In my opinion, it’s practice makes perfect with WordHoard.  The more you use it, the better results you will receive.



Initial Frustrations and the Hopeful Search for Results with TAPoR

For the analysis of Hamlet 3.4, I have been tasked to work with the tool TAPoR in order to pull out some results. To be perfectly honest, I am not happy with this tool so far (as you may have guessed from the title…). I suppose I should start by explaining that I am not a computer person; I prefer doing a close reading of a text with my own mind rather than with a tool. But then again it is nice to try new things, so I figure I may as well try. Granted, not all new things go over well. This is one of them. The limitations I am finding in the tool far outweigh the things it allows you to do. So far, the limitations I am noticing are:

  • The obvious lack of the human imagination. It’s all data, data, data when it comes to a machine, meaning you will miss out on a sort of open minded analysis. I am noticing that the tool is pulling my focus away from the text I am analysing. I am focusing on the results I pull rather than pulling out my own ideas from the text. This makes me feel as if I have blinders on and I’m only able to view the text in this narrow frame of view, unable to grasp everything that is being said by the play.
  • This is a tool which is very user unfriendly, making it a very frustrating thing to work with. I am not saying this simply because the layout is a tad bit dated, but it is sometimes incapable of processing the analysis you want, and instead gives you many error messages.
  • When I am able to have results produced I am unable to save them. I know saving is possible to do because there is a space on the work bench for saved results, but I can’t find anyway of saving my results. What I’ve had to do so far is copy and paste the results into a document and save it like that.

The tools you are able to work with have their own problems, in that they do not do much in the way of analysis results.

  • The tools I have been fiddling with are the word cloud as well as a listing of words, both of which are useful in pulling out key words and themes, but that their extent. I am given a list of words and I am left sitting here thinking “okay now what do I do with these?” I would find it better to go through my text with a highlighter, where I could pull out the same results.
  • The number and distribution with the list of words is lovely, but unfortunately it only shows a distribution chart for the first few terms listed.
  • Searching words is a tedious task, as it does not search through lemmas. Rather, I have to search related words individually. Which, needless to say, is a pain. But I’ll say it anyway.

In general, TAPoR is very much a qualitative tool. It can analyse a text with various tools which produce a list and number of words. With these words I am tasked to sort through the list and find similarities in usage. In the end, I must go to the text and pull out quantitative thoughts with what the text is saying. The one obstacle I have to overcome is that of shifting my mindset away from my own close reading, and letting the tool pull out the key terms for me. From there, I suppose I would go to the text with those results in mind and attempt to pull out a deeper meaning.

It is my plan in analysing scene 3.4 to use my tool to answer two queries:
1. What is the mood and theme of this scene?
2. What is the relationship between Hamlet and Gertrude?

It is my task to figure out how I will go about answering these questions using my tool and hopefully it will produce results that are less frustrating than the tool itself. Wish me luck!


The Frustrations in the Process of Discovering TAPoR

Admittedly, I was not thrilled to be using TAPoR for the first phase of this project.  From our work in the workshops, I was mainly interested in the potential to turn text into visual graphs and tables.  Sadly, TAPoR’s strengths do not lie in this area.  For example, TApoR’s List Words tool allows you to find the number of times a word appears in a text.  However, the results do not come in a pretty diagram or table, just a boring table:

List Words Results

In a way, TAPoR reminds me the first version of Widows Vista, it has lots of buttons and useful features, but it is not very user friendly, particularly to a person like myself, who didn’t even know the difference between XML and HTML until a couple of weeks ago.  Safe to say, finding a place to start was a bit of a daunting task, but the group and I decided to start within the physical text to find a question to focus on.  In my case, I decided to explore the relationship shared between Hamlet and Gertrude.  I believe that Hamlet’s feelings for his mother differ from Gertrude’s feelings for her son, and I want to explore the ways in which TAPoR can help me further examine and prove this theory.  However, before I begun to tackle this obstacle I wanted to isolate Gertrude’s lines from Hamlet’s to examine each individually, a task that has become my central problem over the last few days.

The problem with TAPoR is that it has many available tools, but to a person just becoming familiar with analysing texts in the digital humanities, reading the titles and descriptions of the tools is a bit like reading a foreign language.  For instance while going through the tools I came across the Tokenizer Tool:

After reading the description I was still slightly unsure as to what the tool did, but it sounded like it might help with my task and I decided to try it.  When I did, I was confronted with a screen that asked me to fill in attributes such as the “Tags,” “Token type,” and “Token type option,” the only problem was that I had (and still have) no idea what any of those mean.

After reading the help icons, I put in some information (above) that I assumed correct and was presented with 0 results.  It was slightly disheartening, but I continued spending the next 15 minutes trying different variations of words and googling unfamiliar computer terms.  Unfortunately, I still achieved nothing and was left feeling very frustrated.

It was only after going through my notes that I remembered Professor Ullyot mentioning the “Extract Text (XML)” tool.  After putting in the information as follows:

I finally got a result that isolates only Gertrude’s lines in Act 3.4.

It was at this point that I came across another problem: how to save results.  The Rockwell video mentions saving results to the data bench via the research log, but I have been unable to find the research log function he referenced.  Instead, I have been copying and pasting results into a Word document to keep track of my results.

My experience continues with various setbacks and frustrations, but I am hoping to continue exploring Gertrude’s and Hamlet’s relationship by looking at the distribution of words in their individual lines as well as the shared words and collocates between the two of them.  Hopefully I will come up with some rewarding results.

My experience with Voyeur..


After experiencing a quick glimpse into Voyant after the workshop in class, my anxiety began to grow as phase one of group projects grew closer and closer to the start date.  After meeting with my group to finalize the group contracts, we decided for our next meeting that each member must find something new about Voyant. Overwhelmed with the complicated template of Voyant, I didn’t know where to begin. I didn’t have a starting point, a research question, nor had I had much experience.  I decided that I would work off something I knew for sure, the themes in Hamlet.

To begin, I enabled the stop list and was quickly surprised by the words which were most frequently used according to the word summary. Gertrude and Hamlet both showed up 25 times, good 10, bad 3, love 4, sweet 3, madness 5 and mad twice.  Not only is this list surprising, but it also demonstrates the themes and context of scene four. I wanted to further investigate the theme good vs evil. While referring back to the Hamlet textbook, I recalled this scene being a dark, less loving scene. So why words such as love, sweet and especially good, showing up so much more often than darker, evil words?

Voyant has the ability to search a word, click on the word in the frequency list (to further investigate its location), and it also shows the context of the word in its original sentence.  After further investigation, I found that “good” was appearing more often because Voyant was picking up on “good” in “good  night”,  which was used five times.  There went the support for my good vs. evil theme.  One downfall to Voyant already, is it picks up on words in the results that might not been expected.

Mad and madness in Hamlet is another huge theme of the play.  I wanted to look into who first used this word in the scene, who said it most, and I also thought maybe I could determine if Hamlet really was mad. I compared the two words, mad and madness with Hamlet and Gertrude, and I was surprised to find out that Hamlet uses mad/madness a total of five times, while Gertrude uses it once.  To me, this suggests that in this scene itself, Hamlet demonstrates he is mad by constantly hanging on to a comment his mother originally made.

Working off of mad and madness, I was led to question the validity of Voyant. Was Voyant counting mad in the word madness? After referring to the keywords in context menu, I learned that Voyant only searches for specific words you search for. This is a downfall to Voyant, as I mentioned before with the good vs. evil theme.  If you’re looking for more than just a root word, you need to specifically search words. For example, words ending in “ing” “s” “ness” etc. do not come up.

Although I have only mentioned the basic tools that Voyant offers, there are a lot of hidden visual options as well. At first our group was using Google to find the location of our additional tools, but with further investigation we found out each user is able to personalize their template by selecting or removing any tool. I found this to be very overwhelming, but these visual tools and extra options may be beneficial to those who enjoy tools such as word clouds, and line bubbles.

Custom Template Example

Custom Template

List of  Tools: the Google list:

As Voyant at first seemed very difficult to use,  with time I picked up very quickly on the basics of this program. I am excited to see what more this program has to offer and what more there is to be investigated on Hamlet. My next step is to find a hypothesis or theme within this scene, which will become the basis of our presentation to demonstrate the advantages, disadvantages and use of Voyant.

Ps. The Voyant group is working off a custom version of Hamlet  — Has another group found a way to separate characters speaking versus characters names being mentioned?


– Carly 🙂

Could WordSeer be the simplest word analyzing program?

After hearing my classmates responses to their word analyzing programs in class the other day, I can honestly say I am lucky to have been assigned to WordSeer. WordSeer is simple to understand and easy to navigate. When we were first asked to watch the demonstration videos posted, I figured WordSeer was just like all the other programs we had looked at. Over the past few weeks I have begun exploring WordSeer; figuring out its capabilities, setbacks, and unique features. One of these features is the visuals it creates with just the click of a button. The “Heat Map” visual creates blocks of colour, each one indicating when a word appears within a text. You can choose which text—in this case Hamlet—you want to specify the search for, or you can choose more general and incorporate all of Shakespeare’s work. For example, in the first Heat Map I have used the word love as described in any relation to the word. Here are the results:

As compared to love in Shakespeare’s “Primarily Love” plays:

A unique function of WordSeer that is not used among the other word analyzing programs—that I am aware of anyway—is the related words function. I am guilty of right clicking in Word documents to find synonyms when I am stuck, and “Related Words” does just that. For example if I searched death throughout Hamlet but did not yield many results, I can click on the word and—similar to a Word document—search for synonyms.

One of the only problems I have encountered with WordSeer is the program is sometimes unresponsive. I have had issues with freezing on the website and computer, and more than once it has stopped working all together. At times nothing will happen when a button is clicked on. The only solution I have found for this problem is switching browsers. Personally, I find Google Chrome works best, although I have heard from other classmates that Firefox is also a good option.

Although I have explored the majority of WordSeer, there are still some features I have not thoroughly looked at. The snippet feature is still a mystery to me and although I have tried creating a snippet it usually just highlights the entire play, the exact opposite of what I intended. Exploring Hamlet as a whole has been quite interesting, and narrowing it down to a single act and scene, will be a nice comparison.

Overall, WordSeer has impressed me with its abilities. I am still new at the whole “Digital Humanities” thing, and computer programs follow closely behind. However, WordSeer has been easy to navigate, and even in one week, it has created new insights into Hamlet that I have not previously encountered. I am still amazed at the fact that WordSeer is able to analyze parts of Shakespeare’s work in seconds; I only wish I had known about it when I was in high school and Shakespeare was like a foreign language to me.

Voyeur: My initial thoughts and responses in Phase 1

Before I began working on this project I did not look at Voyeur at all except in the work shop when it was briefly explained.  All I remembered from the workshop on Voyeur was that there was some sort of bubble chart and tree chart involved.  When I began to fiddle around with Voyeur (or Voyant) I quickly realized there was far more to Voyeur than a bubble chart.  My group and I discovered it was actually a median with sixteen tools that allows you to customize your own page to how you would like to analyze the text.  These sixteen tools are all very similar; they differ mainly by frequency and visual elements.  You just choose your own tools and create your own page.  So if you are someone who likes to compare words, characters, and themes with more of a visual component then you can customize the page to fit with your choice of visual tools.  If you prefer frequency charts and specific numbers, than you can analyze the text with the frequency tools.

Once my group and I began to explore Voyeur and all the tools on our own, we all found it to be very user friendly.  Words are easy to find and compare within a large text by clicking onto it in the text or chart.  Voyeur highlights each time the word appears within the text.  If you clicked on “love”, for example, in the word cloud or any other tool you choose to use, it instantly highlights the word ‘love’ each time it appears.  You can upload your own pdf files into Voyeur to analyze it or you can copy and paste the links.  Voyeur also allows you to take away any words you do not need.  For example, if you upload 3.4 of Hamlet, words such as “and”, “it”, “I”, and so on appear as most frequent.  However, you can take those words out of the text by using an option to do so.  This then allows you to see the important themes more clearly in the visual and frequency tools.

One of the major downfalls I found with Voyeur is that the program does not give you any clear directions to follow.  You have to play around with it and not get frustrated when you cannot figure something out easily.  Another disadvantage is that the frequency of a particular word might not be accurate.  For example, my group and I compared the words “good” and “evil”.  ‘Good’ appeared more frequently than ‘evil’ on the word cloud tool.  But when we looked at the actual pdf text we realized that Voyeur was picking up on ‘good’ in words like “good night”.  As you can see, this can be a problem because if we had not realized this we would have come to the conclusion that ‘good’ as a theme is spoken more often than ‘evil’.

My main goal now is to come up with a clear hypothesis to focus on in 3.4, similar to how we focused on the Oedipus complex in our Wednesday lecture, so that I can find out more glorious things about all that is Hamlet with the help of Voyeur. Here is a link to all the various Voyeur Tools that I mentioned.  You can see an individual image of each tool if you scroll down.  Check it out!

Birth of a Salesman: How Word Seer and its Supplemented Images May Sell Us New Interpretations

     Dane Thibeault

English 265 Phase 1 Blog Post 1

   In being tasked with studying act III scene iv of Shakespeare’s Hamlet using the tool word seer, I was prompted to inquire more
about the tool itself, and to convey the results as the basis of this recollection.  However, a question I asked myself, prior to exploring the functions of word seer, was whether I believed it to be a simply interesting device, or an actually insightful device. What I mean by this is that I felt it necessary to deem whether or not the tool would return simply quantitative results with little meaning out of context, or rather, whether the device would return results that could be implemented in forming a qualitative conclusion, one that may not be easily reached from simple traditional close reading and text analysis.  My answer: quantitative results can form qualitative features, in identifying frequent words that may be used in establishing themes of studied texts, and word seer is an excellent tool in doing just so, through its visual functions.

     What are word seer’s limitations? That which we all possess: human intellect. How these problems may be overcome will largely be the target of my research. What I mean by this is that, while data figures may prove useful, they are not interpretations on their own, which can only be achieved by thoughtful evaluation. However, this brings me back to how many of the functions of word seer are a step in this direction. This being said. the more specific limitations of this tool I have yet to discover, and will attempt to uncover in further discovering how it works.

     The initial appeal of word seer, one that I feel deems it as more useful than a series of other digital tools, is that it is equipped with a heat map function, which allows for it to display the frequency of certain inputted words as they appear throughout a text, a visual feature that allows for comparisons and contrasts to be established.  For instance, a constantly recurring word can be inferred to represent a central theme within a text, as a word such as “lust”—one carrying thematic implications—may recur in one of Shakespeare’s other texts, such as Othello. Therefore, to test the validity of this feature, I inserted the word, “revenge” in the context of Hamlet, and was somewhat astounded by the results, (featured below) as they were characterized by a surprising lack of frequency of the word, contrary to my expectations, demonstrating how data features may be used to either verify or discredit superficial suppositions.

I was also surprised to discover that other words characteristic of Shakespeare’s works, excluding “love” and “death”, such as “chaste” “debt” and “honour” were remarkably less frequent than I would have previously anticipated based on my avid reading of the Bard’s other works(this test is featured below). This phenomenon may be used as evidence to suggest that Hamlet may differ greatly from the other texts in the Shakespearean corpus: an intriguing avenue for further research. Such questions as these may not only be tested with tools like word seer, but also, may be prompted by unexpected data results that are returned from such devices.

Therefore, it is now evident to me the profound impact of images on research. While one could repeatedly read Hamlet for analysis, it is unlikely that they could reach such observations so quickly and efficiently, and the further questions prompted by viewing an image would likely be absent from the process of inquisition.  Ammunition to support the significance of images to the process of research and interpretation, and the formulation of new theories and observations, is offered by  Martyn Jessop, in the following  article blurb:

So what to make of all of this, then? Essentially, what I wish to offer is an alternative approach to acquiring information about the themes of commonly studied texts. Therefore, word seer is an effective implement, as it allowed me to conclude, through preliminary trials, some potential ominous themes to characterize the text of Hamlet, through the words frequently used, such as how the repeated use of the word “death” has become an iconic thematic assumption of the play.  Further, this being said, what I desire to advocate is that word seer is made effective by its heat map image qualities, for comparison, contrast, visualization and frequency,  and that the further aim of my research is to continue to inquire into its other potential uses, to further determine its qualitative, insightful potential. Therefore, my next order of business is to explore other such features, such as the word tree below.



MONK basics

MONK (an acronym for Metadata Offer New Knowledge) is by the developers of WordHoard.

Start with this tutorial. Here are some notes:

  • MONK’s capabilities are summed up in the word “metadata,” which essentially means data about data. Parts of speech and lemmas are different examples of metadata.
  • For example, in the phrase “the Thames ran softly,” we know that ran is a verb (specifically, the past participle of to run); that softly is an adverb, modifying ran; that the Thames is a noun (specifically, a river in southern England).
  • The tutorial tells us that MONK treats all texts as “bags of words.” Think of these like bags of Scrabble tiles, but where every word is copied onto multiple tiles. Continue reading

Voyeur basics

Voyeur (which was rebranded “Voyant” in 2011) is a suite of text-visualization tools, by the developers of TAPoR (Sinclair and Rockwell). Here is the material we will cover in our introductory workshop on February 10th:

  1. To learn this tool, begin with a “Quick Guide of Voyeur for Users.” It offers an overview of (1) how to get texts into Voyeur, and (2) how to view different kinds of results in Voyeur’s interface.
  2. The next step is to get texts into Voyeur. Here is a video explaining how to do that. We will work with these files of Hamlet.
  3. After that, you’re ready to view the results. Here is a video on the various tools that Voyeur gives you. Continue reading

WordSeer basics

WordSeer is a program for searching for words and their uses/relationships in all of Shakespeare’s texts, and for visualizing search results in ways that provoke new understanding and new questions. The designer is Aditi Muralidharan. In my notes on each of her videos below, I’ll include a series of questions about Hamlet that WordSeer could help you answer.

This is an introduction to the program’s capabilities and features.

Part 1 covers simple search, for word frequency and location, and relational search, for grammatical relationships (e.g. words describing other words). Continue reading

WordHoard basics

This Wednesday, in TFDL 440A, is the first of five scheduled workshops on text-analysis tools in English 203. I’ll be joined by two research assistants for the course, Sarah Hill and Sarah Hertz.

We have a few goals for the workshop. After you finish reading this post, click on the 5 links in this numbered list for answers to these questions, and our required readings for Wednesday. Continue reading

File for Encoding Workshop + Exercise

Here is the XML file we will use for the encoding workshop and exercise, starting next week.

After you open this link, choose ‘Save as’ in the File menu to save it to your computer.

To view it, you will need a text editor (which is not a word processor). The standard editors are Notepad (Windows) and TextEdit (Mac). I like and use the Oxygen editor, which you can download for free, but which is probably more complex than many of us need for English 203.