How to Write a Blog Post (In Three Easy Steps)

[This is written by Madelyn Brakke, a student in English 203 in Winter 2012.]

When Dr. Ullyot first told our English 203 class that we would be writing blog posts for the course I was a little sceptical. I had never read many blogs, let alone write one. I’ll admit, my first blog post was a little rough, however with each post I began to get a feel for the unique style of writing required for blogs. I also came up with a quick guide to writing blogs, that I hope you will find helpful.

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How to Write a Blog Post

Writing blog posts as assignments for classes is, to most, a foreign concept. Raised from the standard depths of five paragraph essays complete with an introduction, body paragraphs, a conclusion, and to the point of a concise thesis, some are intimidated by the idea of writing anything different. Well, fear not! Blog posts are fundamentally very similar to the standard essay that many of us have written time and time again. Like essays, your posts have a reader, and you introduce, explain, and conclude your topic of discussion. The only differences are that, in an online community, your posts do not just have a reader, but they have readers, whom will all be interested in what you have to say because you will explain and explore your ideas. How do I know they will be interested, you ask? Here’s how:

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Endless Context: the Future of the Digital Humanities Ringing in the Digital World

An Introduction

Every time I hear the words “Digital Humanities” I cannot help but think it is some little subset of the DigiWorld. As I have already mentioned in this course, the Digital World of Digimon is the product of massive amounts of information being packed into data, and eventually having enough information to simulate a world of its own. In my opinion, this is not so far-fetched. Take a moment to think about the Internet. There is nothing else that can hold such a massive amount of knowledge, and that is accessible to virtually any person at the speed and ease of the modern digital world. The knowledge comes directly form people who write about life, the planet, it’s functions, and everything their imagination can contribute beyond that. What the Digital Humanities actually is would be the branch between literature and technology. It has existed for ten years, maybe more. On the other hand literature is something that has been around since almost the beginning of recorded human history. It has had thousands of years of development in style and use, but also in cultural development. People have always had personal and historical inspirations for writing and because of this the context of even a single piece of literature is practically endless. Before the Internet, this context and background information was only accessible in physical form or within a great memory. However, by the incredible developments of technology, the Digital Humanities were born making years worth of physical texts into easily accessible data. Suddenly a text from approximately four-hundred years ago is instantly available and so is the history, interpretations, context and author’s biography with a few simple clicks of a button. This is what Sharon Leon is expressing in her post about if the Digital Humanities continue to expand information for countless users, then they will soon become the main resource for study in a given field; however, they will never replace the human aspect of comprehension.

Something New

The introduction to the Digital Humanities was a bit of a shock. For someone who simply adores the books and hours of cross referencing, it was almost unpleasantly simple to find a text in seconds immediately followed by various tools of text analysis. What would be gained by leaving all the work up to the computer and only using our gift of understanding to analyze Hamlet? However, it was no easy task. There were many searches to perform, and many results to be had, but the problem was what to do with them! From word frequencies to comparing Shakespeare’s entire opus we learned to read data. The best example is the NaiveBayes/Word tree analysis. You input a text, and the meaning you predict to come form it. And you get…

What exactly? At first glance this looks like a jumble of gradients and ratios. It looks like maths with visuals. The reactions were of course:

  1. What is Maths doing in the Humanities?
  2. What does it all mean?
  3. Why does it have to mean anything when we could just read instead?

In fact the word tree and NaiveBayes are ratios, probability, and percentages! As a group full of English majors we were both fascinated and terrified. (Link to second blog post) We had not yet deciphered this information, and we had not earned it; therefore, we did not understand it.

Luckily for us, Dr. Ullyot explained in our first few laboratory classes how words and speakers can be tagged. Voila! Instant understanding gained = instant credibility! Thus never caused a great tragedy, but we did need to learn how to link that data with our understanding. Eventually, and with a lot of perseverance we did. Some very cool things we found out were how to compare word counts between Shakespeare plays using the “comparison tool.” Imagine doing that by reading!

In other words Monk served us very well despite being the professed prototype of DH pioneers, one that was soon forgotten due to frustration. The unavoidable thing about frustration; however, is that it tends to lead to broken things… and the great thing about it is that broken things father ingenuity! Phase two was the reveal of all the ingenuity that followed Monk:

Then Monk met TaPOR, Voyeur, Wordhoard and Wordseer. The great discovery then was that each tool, whether cryptic or simple, supplemented the inabilities of the next as seen here: <>  Each partner had learned to link the data being uncovered to understanding, and we successfully delved into a theme of Hamlet that particularly interested each of us. The theme of Hamlet’s madness enables us all to utilize our tools strengths, whether it was searching for a speaker, someone described, or how much madness was in a particular act. Our Act 3 ended up being the maddest of them all including lines like: “That I essentially am not in madness, / But mad in craft” (Act 3, scene 4).  We ended up using such discoveries from Monk as a starting point. The NaiveBayes tool provided us with direction on what, through association, could relate to our search about Hamlet’s madness, and the Monk concordance searches could find a bit of context. From there we could use one of the searches in Voyeur or Wordseer to place it in the text. One of the most intriguing results we found this way was that the tongue and the sword were both spoken of as weapons.  This started a whole project on poisoning the ear with words, and the damage caused by lies and words. Obviously: “The courtier’s, soldier’s, eye, tongue, sword…” (Act 3, scene 1). In the end it turned out that a bunch of English students could learn to leave the searching up to the tools, and to focus on comprehending the results.

400+ years

What would Shakespeare have done if he were to find there was a technology to break down his entire works into categories, word count, or frequency? If there were something to link all his meanings together? He would probably rewrite his plays to make them that much more cryptic!
The wondrous thing about his plays is that they were even complicated for the time they were created. Nowadays there are scholars who devote their lives to discovering the meanings of Shakespeare and the voices of Shakespeare. The article I chose is speculative on the future of museums and archives whether it will be possible to provide on-site enough information to let the average viewer read a work of art or historical artefact like an expert. She imagines a world where information is immediately available to those who seek it. It sounds like the future for those who would take the time to pursue it. I believe this is the future of the Digital Humanities. It would not be that while reading Hamlet notes appear at the side of the text to divine meanings. There are already books suited especially for that. Instead, this would have the power of the internet behind it. All of the searches we can do in the five tools are to deliver what you are searching for in their location, location, location. Sometimes you can even figure out who delivers the line, how often and if similar words were delivered. It is up to us to understand it. The difference, and what I believe is the future, would be to deliver context to the seeker. Not just the immediate ability to see the context and meanings of Shakespeare in notes by previous scholars, but also maps of discovery. What this would mean is that a person would find Hamlet in a digital tool, and not just find a word. The word would come with the initial and evolved definition since Shakespeare’s time, any idiomatic references it may contain of the 1600s, and the option to dive deeper in to what other scholars think about it. From this our understanding would not only be our own limited experience. Sharon Leon wrote:

“The difference here is in the effort to bring together evidence in a user interface that allows for the consideration of many perspectives and multiple causality, as opposed to offering a single perspective that simplifies the past” (

This would be the ultimate information sharing. Anyone could learn about anything. It would open the flood-gates for textual analysis over the internet. The amount of information eventually becomes its own little official world of Shakespeare. If you remember one of my previous posts, Digimon and Divination (, this is a continuation of my theory. Not necessarily that the DH world will become a different dimension where small monsters run around (even if this sounds accurate for some of the plays), but that any structure of a certain size becomes official. For proof, just look at the recent additions to the dictionary (e.g. to heart as seen here:   The digital humanities may very well become the official source for literary scholars. Although the Humanities, like everything, will become digital it will never actually lose its footing in the physical world. The world of Digital monsters careened out of control because it lost its basis in the physical realm when the programmers abandoned it. Really though, the Humanities will always exist through humans because that is where the value lies. Besides all that the Digital Humanities will never lose its base as long as books still exist in paper… and let us face it; are there really any humanities scholars who do not adore an old fabric bound, gold edged novel from a by-gone era?

For the Love of the Digital

The next question may be… one I have already asked. “Where does the world end and data begin?”
The most shocking thing about computers is really how ridiculously simple they are at their very base. They just constantly make decisions. 1 or 0? Seriously, that is all they are in essence. So what is so complicated about that? Well you should see the extent that it goes to! Have you ever seen a software engineer’s homework? I have, it does not look like it has ANYTHING to do with 1s and 0s. What I do understand about computers and the Internet is, of course, the humanity of it all. I quote myself:
Internet, and the Digital Humanities; “must hold significant portions of the literature that shapes the world we live in. Literature is made in the image of the earth and of human experience, and the characters that inhabit it are in the image of its creatures. The depth that it reaches to is too far to count. It is too far a stretch to say that the universe of data is alternate to the universe of reality?” (

This is where the appearance of math I mentioned earlier meets that of the humanities. People are fantastic at taking literature and finding meaning in it. Computers are simply made to learn the basics of our patterns of association, so both must contribute. Monk, TaPOR, Wordseer, Wordhoard, and Voyeur can show us what they find, but without knowing how the user cannot appreciate the results. Although we are miles away from writing these programs ourselves, at least we now understand the power we are accessing.
It is incredible how much can be stored in virtually no physical space. It probably would blow Shakespeare’s mind. However, this wonderful thing has its demons. If people can burns books and art to erase ideas, then how hard could it be to highlight and delete…? Fight Club had a point: if you erase all proving data of debt, does it still exist? Banks already lend money that does not exist, making 100% plus interest of it back, effectively stealing from you for using a service.

Anyway, that was a tangent, but hopefully it gets the point across. Things that do not have root in the physical world have no credibility, but the Humanities will never survive without human interpretation. A computer can do whatever it is told, but at present, it will not understand why or how. You can tell it how to find the word “cowardly” and that sometimes “yellow” will mean the same thing, but it will not be able to distinguish when. Nuances are another thing that might never be known to a computer. Also idiomatic meanings, connotative meanings, emotional effect, so the list goes on. In Monk workbench even, you can search for lemmas of “madness” and you will be lucky if it comes up with anything about possession. However, in context, as the Sharon Leon (“Content and Context”) this will be the future. Even then the computer will not care. There are in fact many businesses and services that have gone digital beyond the need of human input. Luckily, this will not be one of them. The humanities have always been rooted in the realm of human experience, in passion, and in literature. As you can tell by the very word “humanities” it will never extend out of the influence of human intervention. The “digital humanities” depends fully on the cooperation of the digital and physical, the computer binary and the abstract human brain, and the fabrications of both. Thus at least there will always be the credibility, and always be the earned knowledge.

And So…

The introduction of the Digital Humanities has been like no other experience. Having comprehension transcend physical books was a scary idea, but I understand now that neither the DH nor literature can exist without the other. Reading will always have understanding and relation to fuel it, and the digital humanities will have its massive stores of data and the ease of accessing it to continue with. Thus the use of the five tools has become a triumph, and it will continue. Since understanding will always be required, the Humanities can march on to provide endless rounds of data association with works of art, literature and artefacts, and no meaning will be lost. Hopefully this is the pure future. Information will be accessible to everyone who chooses to find it, and not just through months of study. There will never be any loss of credibility because only some will choose to understand it fully. And the parallel universe made up of our data about the world we live in will never materialize with digital monsters and a doomsday prediction because… actually I cannot promise that one.

Works Cited:

Ann Thompson and Neil Taylor, eds. 2006: Hamlet. The Arden Shakespeare. 3rd

Series. London: Thomson Learning. 613 + xxii pp. ISBN 1-904271-33-2

The Bridge Over Troubled (Digital Humanities) Waters

My Evolving Perspective

Four months ago, I thought I had a sense of how one usually studies a work of Shakespeare: you read the text, read all the footnotes, occasionally pull out the highlighter or scribble some notes down here and there.  After completing English 203, it’s safe to say that I really can’t imagine going back to studying a Shakespearean text by only doing a close reading of the text.

I’ve now been exposed to this new, exciting concept of digital humanities, and in my mind things can go nowhere but up from here.  I’m not trying to be cheesy when I say this, but digital humanities genuinely gets me excited about studying a Shakespearean text.  Although we are still at the beginning stages of this form of study, I truly believe it has so much potential for the future.  I used to almost dread studying a new Shakespearean play because I would usually read the whole thing and often need a lot of external help to grasp the main concepts.  I would borrow study guides from the library, watch the films, everything.  But now with digital humanities tools and the masses of opinions and findings posted online, I can tap into a vast ocean of information that can further my learning effectively with a few clicks of the mouse.

Movement vs. Extension?

Many scholars such as Ted Underwood and Feisal Mohamed have begun to argue, however, that “digital humanities is not a movement” but a “natural extension of the work that bibliographers have always done”. You can find a list of articles and different opinions on this subject by going to Digital Humanities As A Literary Studies Movement: Editors Choice Round-Up. I agree with the statements made by Underwood and Mohamed.  Just because we now have the technological ability to obtain all sorts of data from a text, it does not mean we should completely abandon the text as a whole or forget where the text came from in the first place. We also now have the ability to share our findings online with the world. Mohamed touches more on the role of digital humanities in his blog post, “Can There Be a Digital Humanism?” and I would like to use the rest of this blog post to express my feelings in response to his opinions on this subject and also share what I think the role of digital humanities should look like based on my experiences with it in English 203.

I just wanted to add a comment here about how much the internet truly is affecting humanities. As you can see above, there are at least five different ways in which you can read or respond to Mohamed’s thoughts. These social networking outlets like blogging, Facebook, and Twitter allow so many more minds to be connected and thoughts about humanities to be shared to a wider audience through the power of the internet.

Back to the article, Mohamed speaks in agreement with Underwood in saying that digital humanities is not a movement because “it does not offer to reshape the ideas that we carry into our reading of texts and cultures; it offers instead a new and powerful set of tools available to a broad range of existing critical approaches”.

The Tools of Focus for English 203:

  1. WordHoard
  2. WordSeer
  3. TapOr
  4. Voyeur
  5. Monk

The concepts that we base our hypotheses off of when applying tools such asthese to a play such as Hamlet are not brand new concepts.  The tools do not magically reveal themes to us if we have no prior context or understanding of the play.  These plays have been studied and analyzed for many, many years, and without the help of digital humanities tools such as these.  The sudden incorporation of digital humanities tools should not determine the thoughts we have while reading these original texts, but simply help enhance our understandings and reach further in what we already know.

Our Method in Applying the Online Tools

We decided as a group during Phase 2 of the course that we would each pick a character from Hamlet and use our tools in a collaborative fashion to learn more about them.  I analyzed the Ghost’s character, which was a challenge with WordHoard alone, as I was the so-called WordHoard “expert”, but I was able to use in in combination with the other tools to help me. You can read about what I found in my post here.

For example, analyzing Hamlet by hand versus by, say, WordHoard is not impossible but the time consumption it would take to find every instance the word “mad” is used in the play is exponential compared to the 3.2 seconds it takes WordHoard to do it. It also gives me the context of every instance of the word, so I can read the direct quotes relating to Hamlet’s madness instantly such as Polonius’ quote “that he is mad, tis true” (2.2.97) and Gertrude’s infamous realization, “Alas! He’s mad!” (3.4.106). I talk more about using WordHoard’s efficient word-finding abilities for my study of Hamlet in my blog post here. We are living in a new century of efficiency and convenience, and digital humanities is only building on that, extending the processes that scholars have been using for the past century and enhancing it. It’s just like an automatic door; there’s no reason we couldn’t open the door ourselves, as people have been doing for centuries, but technology has advanced in our world today so that we don’t have to manually do as much.  This of course not necessary, but it’s the world we have grown accustomed to.

Applying the Digital Humanities “Bridge” to the Study of Hamlet

No matter how much data a tool can deliver, it is the human mind that makes the connections and helps create a bridge between the quantitative and qualitative aspects of the tool.  With Hamlet, one has to understand the story before plugging in words or drawing out data from the tools to get results that are of quality interest.

This was something my group learned first hand while analyzing the play and I believe it is a perfect example of why digital humanities is more of a natural extension than a movement.  We had all read Hamlet prior to working with the online tools, so we had some ideas about what we wanted to use the tools for. My group member who was studying Horatio found something with her tool, WordSeer, that she had never noticed while simply reading the text.  It showed her Horatio was related to the word “overlooked”.

She took this as a sign that Horatio must have been overlooked in the play, which would in any other context would be a rational assumption.  I had had a lot of success in finding informative details about Hamlet by simply searching certain words and seeing how many times they occurred and where they occurred in the play with WordHoard.  I helped her use WordHoard to search the word “friend” spoken by Hamlet and see how many times he referred to Horatio as a friend, continuing with the idea of Horatio being overlooked.

She gathered the numbers and information she needed, which you can read about in her post here, and used it to prove her hypothesis in our final presentation.  The problem that arose, however, was that we trusted WordSeer as a tool to tell us too much.  The hypothesis she had became discredited when the WordSeer developer, Aditi, and Dr. Ullyot pointed out that the context of the word “overlooked” was not in the way that she had assumed when obtaining her results.

The tools gave her a false impression about the word “overlooked”, and the only way she could have known for sure what the true meaning and context was was to go back to the original text and read it for herself.  I decided to use WordHoard to see exactly what the context of the word “overlooked” was in the play and found the quote to be Horatio reading a letter to himself from Hamlet, it reading “Horatio, when thou shalt have overlooked this, give these fellows some means to the king” (4.6.11).  In my Norton Shakespeare Anthology, there is a footnote on the word “overlooked” and it says it is another word for “read”.  It’s amazing to me that by simply taking things back to the actual book, such confusion could have been easily avoided.

She finishes off a reply to Aditi and Dr. Ullyot after our presentation with a quote that couldn’t be more true: “I suppose this is the first lesson of the Digital Humanities: ALWAYS be sure you are using reliable sources before getting excited!“ I can’t think of a better first hand example displaying the issue Mohamed raises in his blog post than this.

Concluding Thoughts

We directly experienced the importance of the bridge between the human mind and the digital, quantitative aspect of the tools.  We cannot simply trust the computer to tell us what to think.  It can gives us information that allows us to further understand what we already know, but it cannot operate the other way around.  It is a little bit scary to see what the tools are capable of and what problems they could cause in the future.  Writing is an art form, it needs to be understood and interpreted with proper context, and without that one can get a completely false impression about what the text what saying.  This is why we must use this new concept of digital humanities as a stepping-stone, and way to enhance our analysis, rather than abandoning the very text it was originally based on.  To once again quote Mohamed in his blog post, “digital humanities projects often say that they are innovating the way we investigate texts and cultures, though that innovation arises from a set of technological tools rather than an intellectual position” and to that he adds that “the kind of humanism that seems to me to be most valuable at present is that which fully disarticulates innovation and progress; which makes visible the limits of the ideology surrounding technology.” Computers can do incredible things, but they cannot be compared to the human mind.

Again, I do not want to come across as cheesy when discussing my new-found interest in the digital humanities world, but I genuinely believe I learned a lot this semester in English 203.  I was exposed to a whole new aspect of studying literature that I previously had no clue existed, and I am leaving this course hoping to continue my use of digital humanities as an aid my future literary studies.  As my group learned first hand, I am aware that one cannot solely depend on these new digital humanities tools to get you through a course about Shakespeare, or any other text for that matter, but I am 100% certain that collaborating my base knowledge of the original text with these online tools helped me understand way more about Hamlet than I ever would have by only doing a close-reading of the text.

Works Cited

Shakespeare, William. Hamlet. The Norton Shakespeare Essential Plays and Sonnets. Ed. Stephen Greenblatt. New York: W.W. Norton & Company. 2009. Print.

Final Post

Ready, Set, GO

As a traditional reader, one is able to certainty pick up on thematic clusters, interactions, structure and so on, but it isn’t until you start using digital tools, where you are absolutely able to see both qualitative and quantitative occurrences, such as repetition of words and or various references to God for example. Digital tools take the best of both worlds, and slot them together.  So to summarize my argument, I strongly conclude that digital tools are the future, providing aspects of traditional readings whether it be through creating a hypothesis or by gaining qualitative and or quantitative information. However, the combination between digital tools and traditional reading is the most complete way to analyze a text.

The Beginning

Thinking back to the beginning of the semester in January seems like it was forever ago, but it was the beginning of my digital humanities journey. Coming from the lands of computers, blogging, and the creation of internet pages, I could not have ever imagined all the possibilities such tools could offer to enhance my understanding of Hamlet. My initial thoughts were “Professor Ullyot, how could you combine Shakespeare, who literally has a language of his own, with digital tools?”  Was this possibly the most awkward/ complicated combination ever? No it wasn’t, if anything it was a genius move. One thing I learned about reading Hamlet in two different semesters, as I mentioned in my other blog was that reading Hamlet isn’t as straight forward as picking up Harry Potter, and connecting the dots as the story unravels.  Hamlet is a text that one must actively read, while physically connecting the dots via notes in the margins. I did do this in the fall semester; however, I did not dive into the text and ask questions that were deeper than the surface. Or in other words, my interest didn’t lie in creating a hypothesis and making conclusions with solid evidence. While reading traditionally, repetition occurred, God references were used, and various tones were apparent throughout the text, but my questions were: “who cares and why does this matter?”. Through the use of digital tools, I learned that in fact these questions, references, and instances of repetition Shakespeare uses, are important to the text. If anything, they are the most interesting aspects of the text.

For example, although these are not the most interesting results, this tool from TAPoR pulls out names (or capital letters to be more correct) like Mars, Mercury and God. The way that this tool is capable of doing this, may for some reiterate important ideas or references, perhaps like Christianity for example.

Traditional Reading Benefits

  1. You can always trust the book as a correct source
  2. Structure is easily identified i.e.) line, sentence structure, interruptions
  3. Thematic clusters can be determined i.e.) body parts: head, heart
  4. Interactions can be determined i.e.) statements, questions, and answers
  5. Tone and performance is evident i.e.) is a character giving advice? Or is he angry?
  6. Figures of speech: metaphors, similes, double meanings


Flaws of the Tools

In order to use digital tools, you need to be smarter than the computer. Yes, the computer is a fast worker, but its brains do not equal the power of its user. Therefore, you must know what you are looking for, and at times you may need to question your results.  During phase 2, it was not until I compared my findings with other results from different tools (Monk, WordHoard, TAPoR, and WordSeer), that I really learned the downfalls to Voyeur. Quite often, Voyeur could not find words that certainly did appear in the text and in other tools.  The most frustrating example I had of this was found when searching for the word tongue in phase two in act 3. Voyeur told me 0 results, BUT I physically saw the word tongue with my own eyes in the text, and other tools were showing results of these occurrences. Here are three occurrences within act 3, where voyeur apparently was not recognizing any of them. Cool.


The work of Monk

More downfalls…

  1. Error messages are common
  2. Different versions of the text(s) can change your results
  3. Shakespeare’s language versus modern language = problem
  4. Tools search exactly what you type



Warwick writes “the digital medium allows for a more inclusive approach to academic research, whereby users …become part of the process of discovery and interpretation”. Warwick’s words are exactly right, when your chosen tool is willing to work with its user and provide its user with correct results. Digital tools do not give you answers without work, it gives you data. Digital tools, Voyeur in particular, works as a hypothesis generator as a beginning step towards success. This is the beginning of your process of discovery. Right away by just looking at the visual word cloud you are able to see the words that occur most often: HMLT, Lord, love, play, and make. Or if you are a person who is more number orientated like I am, you could use the frequency chart, where numbers are listed by the most frequent used words.

While looking at the frequency chart for act 3, I’ve been given quantitative evidence: love is a word that occurs most (23 times) in act 3.  Although this is an evident theme a traditional reader could have easily pulled out after reading Hamlet, we must remember that we are only in the stage of constructing a hypothesis, where Warwick writes “users of digital resources do know what they need and if they don’t find it they will not use things that are unfit for their needs”.  In other words, digital users will keep looking until they are able to collect the evidence, whether it is qualitative or quantitative data, to make a conclusion. By keep looking, I mean these tools are not capable of pulling out the differences between how the word love is used. Hamlet states, “I did love you once” (3.1. 114) when speaking to Ophelia as way to express an emotion that was once there. In the play put on by the Players, you read “you shall see anon how the murderer gets the love of Gonzago’s wife” (3.2.256). Yes, love is mentioned, but it is not really used in the context of an individual expressing love as an emotion to another individual.  Depending on what a user of digital tools was looking for, the quantitative data could distract you from coming to a correct assumption about love in the play. There are many other occurrences where this issue was present.  Hamlet/ Shakespeare uses the words honest and fair to question Ophelia, when in modern day, these terms are used very differently.  See my blog post for a further explanation and dictionary definitions.

Traditional Questions

With traditional reading though, what would one do with the theme love? We could use qualitative evidence to compare the different types of love? Or analyze how Hamlet uses the word love? Is he really in love with Ophelia? Regardless of the direction one may choose, I feel like a conclusion can be reached, but the so what factor is missing. Why not take your hypothesis to the next level and use frequencies, visuals and chart comparisons to deepen your analysis?

Making Progress

Since we were using digital tools, I decided that it wouldn’t make sense to go to the text we used in class to look for information, and then put it into my program. I tried to stay dedicated to digital tools. Luckily, my tool Voyeur allowed for me to maintain my dedication. Voyeur provides a corpus reader, which is practically the text itself. For some tools, this is where there was some disconnect.  Most other tool users could not a) read an entire act, scene, or play b) modify their text and or c) take their data, and achieve visual results. I believe most students will agree that tools are great for quantitative data, but Voyeur is much different. It combines the best of both worlds like mentioned above. (To be honest, the second half of the semester my text book of Hamlet sat collecting dust). Voyeur was, however, beneficial in the way that I could modify Hamlet to either include, or exclude things that were tainting my data. For example, one of the biggest downfalls to Voyeur was the fact that speakers could not be separated from their names being mentioned. In other words, this was ruining my quantitative data, by making it seem like Hamlet was mentioned 100 times, when over 75% of the Hamlet occurrences was when he was speaking.  TAPoR, however, was the tool which was responsible for gathering when characters spoke.  By separating character’s lines via TAPoR, then putting my information into Voyeur, it was much easier to analyze each character’s word choices, emotions (qualitative) and frequencies (quantitative) and or information.

Voyeur- Results are tainted because the file has not been edited


Because Voyeur offered the ability to read the text through the corpus reader, I was able to gain both qualitative and quantitative occurrences, which I don’t think was something I could have necessarily gained through traditional reading on its own.  Although I wasn’t able to “make notes on a piece of paper, doodle, fold it up and carry” Voyeur with me, like Warwick states when she compares traditional texts with digital humanities, the information I was able to drag out of Voyeur was something beyond any traditional reader could gain alone.

Corpus Reader - Just like a book ...


The conclusions I came to, as seen in my blog, was a combination of reading through the corpus gaining qualitative and quantitative information, then submitting it into the program to further analyze the qualitative data. Even though I was randomly typing in words, checking their frequencies and looking for connections, this would have been completely impossible through traditional reading. Again, I know this because the first time around reading Hamlet, these themes were overlooked, probably due to the complexity the story line or language.  First I noticed that Shakespeare makes references to body parts, for example “go, go, you question with a wicked tongue” (3.4.10), or compares words to daggers, “I will speak daggers to her but use none” (3.2.386).  By slowly typing in each word in search bar that was a part of the body, my phase two group was able to make the theme of our presentation based on senses (eyes, hearing, and speaking/ tongue). Finding this information was new to me. I never would have been able to make the connection between all of the senses, if I did not break down act three, and draw connections through the frequency occurrences.  I think by slotting information into a program allows you to slow down and analyze it in a way you never would. Like mentioned above, without the use of numbers or data to prove your point, the so what factor occurs. I strongly believe that with the help of digital tools, you are able to fill the so what void. It is like science. You make a hypothesis, but until you prove your hypothesis with data and results, it is invalid and useless.

Traditional vs. Digital

I believe that a reader could easily create a hypothesis, compare themes, words and references without the use of digital tools.  However, I strongly believe that with the help of digital tools, their speed, frequency lists, and visuals, can provide that extra bit of information that can take understanding and learning to entire new level. A computer or a digital tool, as we know, is smart, but not as smart as its user. Tools are full of flaws that can often taint our understanding if further investigation is not taken. In Warwick’s blog, she quotes Helen Chatergee who does work at UCL Museums and “suggests that when we handle real objects, different parts of our brains respond than when we see a digital surrogate”. It does not specify how the brain responds differently, but the fact that this quote states that it does, demonstrates that both digital tools and traditional reading used together could provide the most useful results. At least this way, our brains are responding differently to each method to gain a complete picture.


Works Cited

Shakespeare, William. Hamlet. Ed. Ann Thompson and Neil Taylor. London: Arden Shakespeare, 2006. Print.

Warwick, Claire


Concluding on My Introductory Experience in the Digital Humanities

Introductory Conclusions

As an english major, a lover of the literary, historical, and symbolic, I walked away with a celebratory slide, from anything that involved numbers in any shape or form. I suppose in my mind it was a celebratory slide, however to my math, physics, and chemistry teachers, it must have resembled something of a frantic scrambling flee for the door. This is, I think, something that the majority of my fellow classmates in ENGL203 can attest to; The mistrust of anything that would take a piece of literature and suggest, ” sometimes, a river is just a river.The river moves with this speed, this velocity, because the water demonstrates this amount of viscosity, and it moves in this direction.” As students of the literary,  I suppose in response we would go on our rants and tangents of the river representing a winding and continuous process of life. My point here, is that there has been an innate and inherent hatred for some of us, if not most of us, towards the mathematical and statistical aspects of the world, and how those aspects take away from the symbolic values that have been metaphorically scattered throughout the universe.

Throughout the course of ENGL203 however, in the midst of my introduction into the world of the Digital Humanities, my understandings of the statistical, quantitative aspects of the literary text such as Hamlet, has consequently enriched my qualitative findings of the text. Digital Humanities, in my mind was the best example of an oxymoron, if I had ever heard a good one. I began this course with the question, “what could I possibly gain from knowing how many times a word shows up in a text?” I have concluded the course with the question, “in what different ways could these statistics and probabilities be applied to this text, or a wide array of texts, to provide me with the best kind of data to answer a series of research questions?”

Working with MONK throughout the semester in analyzing Hamlet, I have acquired a new appreciation for the mathematical aspects of the world. I say ‘appreciation’ without the implication that I have begun to appreciate mathematics, but to mean that I can see the value that it can provide in analyzing a text such as Hamlet, as I continue to have a lingering suspicion toward mathematics. Ben Schmidt’s article Treating Texts as Individuals vs. Lumping Them Together has provided me with additional insight into my perspectives of the tools that can be used to analyze texts, such as Hamlet, in the Digital Humanities.

It is my perspective, and argument, that although the traditional close-reading that we have been taught throughout the years as lovers of the literary has much to offer us in an analysis of literary texts such as Hamlet, the tools that are available in the Digital Humanities that provide us with statistical data and probabilities complete our understandings of the qualitative with the quantitative aspects. I believe that the precedence we place of the qualitative, though understandable, is misguided. The numerical values that we are provided with in our tools, though frightening and confusing for us english majors, complete our analysis in such a way that makes the digital a valuable and effective method in text analysis.



The Quantitative

MONK, despite its glitches and imperfections, did not fail to teach me a lesson about the Digital Humanities and the value of statistical data. In the beginning, I suppose I did not feel very different from the way Queen Gertrude did when she responded to Polonius’ melodramatic ramblings by saying, “More matter with less art (2.2.95).” I found MONK to be spewing at me numbers, statistics, probabilities, that provided me with nothing valuable whatsoever.

The images below, provide a pretty clear picture of what I was ‘fleeing’ from the rise of my university career:

THIS, after the entire course, is still lost to me:

I initially believed that I was going to understand nothing about these tools and flunk out of the course, however, it was comforting to find that I was wrong.

An aspect of MONK that I found particularly interesting in the way it contributed to my analysis of Hamlet, was the classification tool and its Naive Bayes analytics and Decision tree as methods of analysis. By using work frequencies of a variety of texts, MONK is able to classify texts into categories.

My immediate understanding of Hamlet, just by reading it, is that it is particularly tragic in its subject matter. Hamlet mopes around the entire text, quips like a madman with incredible mood swings, while everyone around him is scheming against one another, only to have it so everyone dies eventually. This plot, as ridiculous as I have made it seem in my summary of it, can be read as nothing but tragic. However, from the classification tool that MONK provides, I discovered that Hamlet‘s word frequencies, were more comedic than tragic. By comparing it to a wide array of different texts, I was able to discover that Hamlet, like other texts such as Othello, are anomalous to the tragic genre of Shakespeare’s texts. The question to be considered here is, would I have met these conclusions from just a traditional reading of the text? I doubt it.

The emphasis here, is not on my lack of abilities in close-reading texts…but on the acute abilities of the text mining strategies of tools such as MONK. From word frequencies, or the quantitative values of Hamlet, I was able to discern the qualitative aspect of it as being less tragic than the classic tragedy in Shakespearean texts.

The Qualitative

In his article Treating Texts as Individuals vs. Lumping Them Together, Ben Schmidt explores and describes the strengths and weaknesses of various methods of analytics, and their use in answering question in text analysis. He states that the key importance in using tools that employ these methods of analytics is “how to treat the two corpuses we want to compare. Are they a single long text? Or are they a collection of shorter texts, which have common elements we wish to uncover?” Interested in analyzing hundred of texts, Schmidt is aware if the imperfections that arise from any division of this large number of texts. He poses the question, ” how far can we ignore traditional limits between texts and create what are, essentially new documents to be analyzed?” At the end of the article, he provides lists of the appropriate uses of Dunnings log-likelihood, Mann-Whitney, and TF-IDF comparisons in texts.

From working with TF-IDF as well as Dunnings log-likelihood in MONK, it was interesting to find that I reached the same conclusions that Ben Schmidt reaches in his article with his analysis of the tools. Attempting to use these analytics in MONK just to analyze Hamlet alone, was a difficult and arduous task, as the text being analyzed was simply to small. Hamlet as an individual text, in comparison to the huge array of texts available in the MONK program, hardly returned information that could provide useful in a text-mining analysis of Hamlet. As many of the MONK users have noted, Hamlet on its own, was too narrow a data set to find any meaningful data using a broad and wide-scale analysis method such as MONK. As suggested in Schmidt’s article:


Each tool that uses and provides quantitative data has individual strengths and weaknesses. The valuable lesson to be taken away from Ben Schmidt’s article, is the suggestion that there must be a certain amount of care put into using tools such as Dunnings Log- likelihood and IF-IDF comparisons, and even with that care, sometimes these tools cannot be applied in the line of inquiry being pursued. In short, these tools cannot alway be relied on, and should not be the absolute basis of argumentation when it comes to text analysis. That mistrust that all of us share toward the numeric values that can pervade the literary, though extreme at times, is not unfounded. There is value in the qualitative meaning that we gather from traditional readings of texts, when the quantitative just simply does not make sense.


I have learned that, in a sense, neither the traditional reading nor the digital statistics of texts are completely trustworthy.

With the traditional reading, I concluded without being absolutely correct, that Hamlet was completely a tragedy, and that there was simply no other type of text that it could be.

With the digital statistics, I discovered that, although I was returned with data, the methods that I was attempting to use were very picky in the type of data I was inputting, and could return me with skewed conclusions if I did not use them with the utmost care. (Which I don’t believe I did all the time.)

However, in both circumstances, I was able to use the digital to correct my traditional reading, and use the traditional reading to double-check my digital findings.

My purpose in writing all of the above is, therefore, to show that there is much value that can be gained from both methods of analysis. Each method on its own, is in some sense, incomplete. The Digital Humanities, in all of the tools it offers to provide a statistical analysis of probabilities in texts, through methods such as word frequencies, has provided not only a valuable, but legitimate method of analyzing literary texts such as Hamlet. Our fear of the numbers in statistics and probabilities and the automatic assumption that they will not be useful in a literary analysis of a text, though understandable, is misguided. As Hamlet begs of his friends, ” Nay then, I have an eye of you. If you love me, hold not off (2.2.255-257).” A request that many would beg of their endeavours using the digital tools, that they would not hesitate to reveal the value that they have uncovered beneath the text. The trick is in recognizing, to begin with, that there is in fact value, it just simply must be uncovered and laid in plain view for analysts to use.

However, once it is found…there is a great amount of valuable knowledge to be gained that can be contributed to our analyses as a whole.

For example,

The river does indeed represent the continuous winding and progression of life, and the numerical values of its speed, direction, and viscosity, tell me that this metaphorical river of life, flows at a rapid pace, in one direction decided by destiny, at a speed determined by the hardships and challenges innate to its path. Thus, providing me with a well-rounded, complete analysis, with the symbolic qualitative meaning and the numerical quantitative data, of the way of life.



Works Cited

Shakespeare, William. Hamlet. Ed. Ann Thompson and Neil Taylor. London: Arden Shakespeare, 2006. Print.

This is Not About Conformity

They say the definition of insanity can be defined as doing the same thing over and over again, while expecting different results.  If this is true, can literary scholars/analysts be classified as insane? Surly not! However, resisting the Digital Humanities efforts to analyze old texts in new ways is most certainly, insane.  The Digital Humanities encompasses a truly revolutionary method of textual analysis by, to put it simply, using computers to study books.  This is an initially intimidating but ultimately fascinating idea.

As a self-professed book-lover, I was, admittedly, skeptical of using a computer to analyze a text.  However, knowing our class would be studying such a historic text (Shakespeare’s Hamlet) I was interested to see how two seemingly opposite worlds could be fused together. Are books and computers even remotely compatible?

The Beginning: A Little Background

As it turns out, books and computers are most certainly compatible! Throughout the past four months, the Digital Humanities has proven itself by providing an array of highly enriching insight as a reward to having an open mind.  As a preliminary example supporting my theory, I would like to draw on a comment made by a classmate of mine, Ruby. In the closing discussions of the English 203 seminar, Ruby mentioned she had previously studied Hamlet four times, in an academic setting. However, it was not until her most recent analysis of the text, integrated with the Digital Humanities, that she discovered new elements. This is because Digital Humanities tools search text in a different way. They conduct searches too time consuming and, frankly, too boring, to do by hand.  Thus, revealing new insights traditional analysis simply cannot, sanely, begin to explore.

That being said, there is a balance to strike.  Digital Humanities tools are useless without a thorough understanding of the text you wish to analyze. To quote Dr. Michael Ullyot, it is about “taking a stupid computer, and telling it to do smart things”.  If you haven’t read the text, you simply won’t have anything smart to tell it to do, ultimately rendering the analysis tool useless and you, well…lazy.

This, I now understand, is precisely why the first portion of the term was dedicated to studying Hamlet “un-plugged” No computers allowed.  After being presented with a steady Hamlet platform, Digital Humanities became less intimidating and increasingly intriguing.

The Middle: Phase One

For Phase One of the Digital Humanities aspect of this class, we were divided into groups of five and designated the “experts” or rather “soon-to-be” experts of one of five tools:

  1. Wordseer
  2. Wordhoard
  3. Ta POR
  4. Monk
  5. Voyeur

As a member of the Wordseer group, I was excited, but perhaps a little nervous, still. What kind of things would we be able to find? Would any of it mean anything?

After a couple hours of acquainting myself with this new-to-me tool I discovered a number of helpful searches available to me via, Wordseer. With fuctions such as “Read and Annotate”, “Heat Maps”, “Word Frequencies”, and “Word Trees” the
results you pull are truly, endless.

This portion of the term enabled me to become comfortable with my tool, and ready to sink my teeth into Hamlet – the text we had already studied “un-plugged”. For more on Phase One, you can read about it from my point of view on my blog

The Middle: Phase Two

After scrambling the students in our class perfectly, five new groups were created -holding an “expert” from each tool and assigned a specific act to focus analysis on.  I was assigned a member of the “Act One” group. In my first Phase Two blog, I wrote about how I was concerned and possibly a little bit jealous of other students with seemingly  juicer acts to tackle. Ultimately, I decided to view my act as a challenge – a “Literary Where’s Waldo” if you will.

We, as a group, decided to focus on character development as a central theme of Act One analysis. In a previous post of mine, I discussed the division of characters and exactly how we set out our “Plan of Attack” (P.O.A). I worked on the
character, Horatio. An interesting aspect I chose to focus on is his friendship with Hamlet. This is where the Digital Humanities really came into play for me. For example, examine this quote:


          Hail to your lordship. I am glad to see you well


          Horatio, or do I forget myself.


          The same, my lord, and your poor servant ever.


          Sir, my good friend, I’ll change that name with you.

This excerpt strikes me a highly interesting. It seems that for someone who is portrayed throughout the play to be Hamlet’s only trusted friend, they have not known each other for long. Hamlet actually checks to make sure he has remembered Horatio’s name. Intrigued by this idea, I decided to dip into the tool, Voyeur, with the help of my group’s Voyeur expert, Ruby.

After conducting a few simple searches, we uncovered something I found significant. Throughout the plays entirety, Hamlet only uses the word “friend” fifteen times. Eleven times, the word is used in a sarcastic, facetious tone while speaking to or of the characters Rosencrantz, Guildenstern, and Polonius. The remaining four times (all occurring in Act One) it is used while speaking to or of Horatio, all in a genuine tone.

Voyeur Charts



Why is this important or significant? Because for someone so unfamiliar to Hamlet (having to check his name), Horatio is proving himself, subtly, to be an important element of the play, all before act two begins.

If it is true that everything a writer writes is intentional, is it possible that Shakespeare was, subtly, very subtly, setting Horatio up as the character to “win” in the end? Despite his lack-of-presence throughout the middle of the play (as displayed/compared with Hamlet in the above graph) Horatio ultimately come out on top, fooling, I assume, most readers/viewers.


The End New Beginning: The Digital and the Humanities

As I have come to find, The Digital and The Humanities can more than co-exist in our world. Together they can thrive.  In the true interest of knowledge, in the true interest of academia, is there anything wrong with expanding the traditional methods we have so comfortably subscribed to? I would have to answer: no. In the interest of learning more, how can utilizing every resource available be wrong?

With an entire community of Digital Humanists out there, The Digital Humanities is an exciting and fresh element of out over-technologically-dependent society.  You know what they say, “If you can’t beat em’, join them!” however, this is not about conformity – rather it is about a sort of unity.  In a previous post of mine, I wrote about how shocked I was to learn the creator of Wordseer, Adidti Muralidharan, was actually reading our blogs!

Holy Crap Squared

I understand that when I click “publish” I am putting my work out there for anyone to view; however, the impact was lost on me. This is, until messages from Berkley, California started surfacing in response to blogs posted on the
topic of Wordseer. Unity.

The World of Digital Humanities is so much larger than it may first appear. Initially, I was under the impression that Digital Humanities covered only the study of literary works for purposes similar to mine – expanding a text, digging deeper into a story, etc. Upon further research, however, I have discovered that Digital Humanities encompasses a much larger scope of research and analysis. It reaches into other fields of the humanities such as Psychology, Sociology, and even History.

Skeptics of the Digital Humanities offer that online sources cannot be trusted. As Anita Guerrini, the author of the article,“Analyzing Culture with Google Books: is it a Social Science? writes “I was immediately struck by what seems to me to be a fundamental flaw in its methodology: its reliance on Google Books for its sample.” I have to admit, I disagree with this statement entirely. I do not understand how using a tool as universal as Google, can be described as a “fundamental flaw”. The word has become a verb due its popularity! (Example: “What is Google? Oh I’ll just Google it!”)

While, admittedly, the Digital Humanities is still in its up-and-coming phase, it is through using tools such as Google Books, (capable of housing a limitless amount of analysis material) that Digital Humanists will be able to continue forward. Expanding, and ultimately uniting academics and scholars with common interests and goals across the globe.

Anita continues “The authors equate size with representativeness and quantity of data with rigor. I am not sure that is true… But some of the results are simply banal”. I have to agree with her…partially. Some of the results I personally came across were boring, pointless, or even misleading all together. This is where the quantitative, scientific values of the Digital meet the qualitative, intuitive values of the Humanities.

Part of using the tools provided by the Digital Humanities is determining what is important, what is new, what is exciting! Users sift through results much the same way texts are analyzed with red pen, stick-notes, and highlighters. This is why I strongly believe the future of Digital Humanities involves a balance both. Books and computers, together.

Further into her article, Anita comments “Perhaps most disturbing to me is the underlying assumptions of such work about the humanities and about what scholars in the humanities do. One assumption is that the humanities need to be more like science and that we need to be more like scientists — that quantitative knowledge is the only legitimate knowledge and that humanities scholars are therefore not “rigorous.” I understand her point of view in terms of the pressures
surrounding the “legitimacy” of the humanities; however, I do not feel as though this is the time to be territorial.

We live in a world where our cars talk to us and where people can carry 2000+ songs around in the pocket of their jeans. We live in a technical world. Is it possible the whispers…or screams, calling the humanities pretentious is related to the social science’s unwillingness to change? For the sake of academia, or research, or simply for the sake of curiosity, why not give the Digital Humanities a try?

The trick to hacking the Digital Humanities lies in the approach. As I mentioned earlier, without a thorough understanding of the material you are analyzing, the digital can offer you nothing. Is it possible that books are not better than computers and that computer are similarly no better than books in regards to yielding the most insightful results? I think so. Perhaps, the ultimate method lies in a combination of the two, a mixture of the traditional and the modern.

When the Humanities can learn to play nice, the resources available to them will be, virtually, inconceivable.


The Digital Humanities and the Humanities: An Integrated Force?

With the accumulating significance of the digital humanities, comes the potential for an integrated, more effective approach to critical text analysis. The potential process arising from this rapidly developing field may be viewed as the following: traditional closed reading will provide the question, and the digital humanities will provide the answer, which may then be formed into a conclusion, following critical qualitative analysis to ensure the credibility of quantitative values. In other words, so long as human intellect is applied to evaluating  the validity of data, the quantitative approaches and results inherent to the digital humanities demonstrate the potential to illustrate new conclusions and questions regarding a text, through identifying patterns and trends which may not have been considered before. Throughout the duration of this account, it is my intent to convey how the implements of the digital humanities may be considered an equal part of the humanities, as opposed to simply an instrument to the broader field—so long as data and quantitative results are applied properly (with sufficient awareness of the potential sources of error in what is being represented). I will demonstrate this level of potential linkage, through first discussing a case study of Shakespeare’s Hamlet and how quantitative and qualitative text analysis may integrate with one another, before proceeding, in the second section, to convey the potential of the digital tool word seer to collectivize subjective and objective material into one unit, before later exploring the question posed by Michael J. Kramer in the blog post Reinventing the Wheel(which may be accessed using this link: ) that I have based my argument on, which is: to what
extent are the digital humanities one with the traditional humanities?
I will then proceed to highlight my reflections, in the final section, on
engaging with the digital humanities throughout the English 203 research-based course, commenting on what I have learned throughout the process.

Hamlet case study- A demonstration of how quantitative and qualitative approaches to a critical question may be applied in cohesion to form a conclusion

Upon evaluating the iconic text of Hamlet, two approaches may be pursued—an application of knowledge acquired through critically reading the text, or an alternative approach, in the case, being the use of a digital humanities tool to suggest trends and patterns that could serve as indications of plot, motifs, and character distinctions through speech patterns. In considering these two potential avenues for evaluating Hamlet, I have considered a question that is often debated, regarding the text: Can Hamlet’s perplexing behaviour be attributed to insanity(or “madness”) or to calculated deliberation? Qualitatively, Hamlet himself offers insight into his motivations for his later behaviour earlier on in the text in stating to Horatio, “Here as before: never—so help you mercy,/ How strange or odd some’er I bear myself/(As I perchance here after
shall think meet/ To put an antic disposition on)…”(Hamlet.1.5.166-70) before instructing his friend not to concern over his behaviour. Additionally, Hamlet also offers another indication that he is well aware of what he is engaging in, and how is conducting himself, when he subtly implies to Rozencrantz and Guildenstern: “I am but mad north-north west. When the/ wind is southerly I know a hawk from a handsaw”(Hamlet.2.2.315-16). How does this information relate to the question I posed? Critically analyzing Hamlet’s remarks for indications of deliberation exemplifies the qualitative approach to answering the question. The quantitative approach, which well supplements the qualitative
approach, may be conducted with a variety of digital tools—I am most familiar with Berkeley’s word seer(, and have therefore implemented it in my investigation.

While it is relatively simple to superficially label Hamlet’s term disposition as a façade or contrived attitude, there is little that can be verified about the statement, in the absence of knowing how the word is applied throughout the text. In order to find out exactly what “disposition” refers to, I found it suited to input the word into word seer’s word frequency heat map function( feature=endscreen&NR=1&v=DPhQQExQjZ4) which enables one to visualize the frequency of a word throughout a text, and identify when exactly it occurred(I will elaborate further on the potential of this feature in the next section) in order to observe the different meanings it represents, and how often it is used. In conducting this assessment with the entire text, I received the following results:

Incidentally, the word “disposition” sparsely occurs throughout the text.  However, in the usage of the word pictured in the above heat map, it appears once again to represent either personality traits, or characteristic tendencies—aspects that could feasibly be manipulated, or otherwise “forced”, as Hamlet is described as doing. This is an effective example of how quantitative figures may reinforce or reaffirm hypotheses or qualitative speculations. While this data is intriguing, I decided to consult another feature of word seer, the word tree function, to see if I could identify the context surrounding the word “disposition”, each time it is used throughout the text. The results I received are as follows:

What I found interesting was that the word truant appeared in the context of one of the uses of “disposition”, another apparent indicator of disposition derived from a tendency. Therefore, a potential answer to the question I posed, harnessing ammunition from both qualitative speculation and quantitative results, could be that Hamlet is well aware of the way in which he is prepared to conduct himself, and is thus entirely sane, and is concocting a ruse to mislead his uncle from his intentions—a deliberative, conscious act. While this assertion is open to re-evaluation, and is not necessarily correct, it provides an optimal example of how the qualitative and quantitative can intermingle to produce new conclusions, or otherwise reaffirm them—a product of the digital humanities and the larger field of humanities integrating.

Word seer- An efficient companion to my research

Berkeley’s word seer, a relatively simple to use instrument, is most useful in its capacity to transform raw data into new questions. What I mean by this is that the tool demonstrates  the potential to reinforce or generate qualitative hypotheses, based on quantitative data returned—as, when one employs word seer in their research, they are often not sure as to what they will find. In a previous blog post( I discussed how word seer is both interesting(through its visual qualities) and insightful(in its potential to produce new interpretations, or disregard obsolete preconceptions), and how superficial suppositions(such as Hamlet being considered about “death” or “revenge” alone) may be discredited based on the actual word frequencies of such words, as revealed by word seer. For instance, in regards to the qualitative value of the tool, my initial observation(expanded upon in my blog post cited above) was that “…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…”. However, the perils of accepting these words as themes without critical analysis and the application of human
intellect is illuminated in Michael J. Kramer’s admonishment that “…even as we find ourselves experiencing the new, it’s just as worthwhile to locate Digital Humanities in relation to the old.” In this case, the “old” is traditional text analysis, which must not be neglected, even though word seer and its aesthetic visual qualities(such as heat maps and word trees) offer an intriguing alternative. This is yet another example of how the quantitative and qualitative must work in cohesion—in this case, mutually offering insights towards one another, as data may prompt new questions, which may then be viewed through a closed reading lens, considering specific thematic and plot aspects of a text.

In responding to my assertions of the methods that must underlie the tool word seer, one might contemplate this question: What evidence is there that word seer can aid in disregarding obsolete or superficial qualitative conclusions or hypotheses, surrounding a text? My rebuttal is illustrated in this search of the frequency of the words “revenge”, “murder”, “death” and “kill” in Hamlet, using word seer’s heat map function, with the results depicted below:

Needless to say, Hamlet’s earned legacy, coined by popular culture, as the revenge tragedy is supported by the frequency of the words I selected, as they appear abundantly throughout the text. However, perhaps Hamlet isn’t exclusive to the characterization of “revenge”, as a conducted search of the same words in Coriolanus uncovers somewhat similar results:

These word frequency similarities may serve as a prompt for a qualitative investigation, based on this quantitative data, into what plot elements of each text establish Hamlet and Coriolanus as similar—another testament to what kinds of questions and approaches can be provoked by synergy between quantitative and qualitative methods.

In another of my previous blog posts(, I evaluated, in great detail, the extent to which word seer may aid in determining whether or not Hamlet as a character fits the profile of a tragic hero(compared with the flawed characters of Shakespeare’s other texts, such as the ambitious Macbeth)through the use of its described as function, which enables one to view the words used to describe certain characters by those around them. In inputting Hamlet described as “blank” I received support for my hypothesis that Hamlet is not as well
defined as other tragic heroes featured in Shakespearean texts
—if a tragic hero at all. Qualitatively, he lacks the tragic flaw that causes him to pay with his life for a mistaken act(while Hamlet dies, it is not directly the result of something he has done based on a flawed character trait, as opposed to say, Othello, who commits the mistaken act of murdering his wife Desdemona as a result of his tragic flaw of envy, and then ends up taking his own life as a consequence), while quantitatively, the data of word seer reveals that he is described as the following(which are hardly terms indicative of a tragic flaw, or character weakness):

In essence, these correlations between a character and how they are described are valuable in indicating not only how they are perceived, but perhaps how they act as well. Therefore, in light of word seer’s ability to perform such searches, along with heat map and word tree visual representations of word frequencies throughout entire plays(or even more compact fragments of acts and scenes),   deems it a formidable and useful implement of the digital humanities. Not only has this tool allowed me to engage substantively with the text of Hamlet , examining details that are often largely overlooked or obscured in the process of traditional closed reading, but also, it has provided me with a medium to blend critical qualitative text analysis with valuable trends and patterns identified by quantitative data. Thus, not only is word seer an effective tool for viewing word frequencies and conducting word comparisons using the simple search feature, it is also an agent of blending the subjective with the objective, in order to aid in establishing new avenues for research. It supports the claim that the digital humanities and the humanities can, and should be(with careful attention being directed towards the quality of data received) a unified force, as opposed to one “serving” the other.

Further exploring the question: Are the digital humanities and the humanities one?

An integral consideration has been articulated throughout this account: that the digital humanities and the traditional humanities are an integrated force—not a superior and subordinate. However, I have also advocated that there are potential hazards to relying too much on data, without stopping to consider its implications, or its possible errors or misleading aspects. I have based this argument largely off of Michael J. Kramer’s Reinventing the Wheel, in which he effectively conveys the responsibilities that are inherent on behalf of the searcher when consulting data results, in his admonishment that “The danger here is that we are not thinking carefully about the framework in which Digital Humanities
might thrive and contribute to society beyond assumptions about technology solving all problems…” This is a highly impactful statement, as it highlights the tendencies, when the digital humanities and their associated tools are enlisted, for users to either uncritically accept data as the truth, or otherwise, dismiss the value of data to the humanities, altogether. An excessive faith in technology, as in other fields, such as science and environmental politics, may often lead to overconfidence in its ability, causing critical concerns and issues requiring intellect to be largely
overlooked. An example, in terms of the English 203 research course, would be accepting the data of word seer and its word frequencies extracted from Hamlet to represent the theme of the text, and the overall message, without closed reading to identify the integral context of the play. While word seer, in revealing words such as “death” to be frequently occurring throughout the text, may allow one to develop the opinion that the play largely circulates around death and murder, without the context achieved through reading the play, these artificial suppositions are virtually meaningless, as these words could conceivably occur frequently in a comedy about love, as well. In other words, data without a context is merely an assumption, even if it closely represents details that are consistent with the theme of a literary work.

“We can be critically self-reflective and move forward,” are Michael J. Kramer’s optimistic words, regarding the digital humanities. This belief conforms to the idea expressed throughout this post, that, with a sufficient amount of critical guidance and thought, data and context or qualitative textual elements can be intimately joined with one another. Kramer consistently articulates the importance of retaining the methods
of critical thinking in traditional textual analysis, well exemplified through his observation that “…there’s nothing wrong with being excited about the fresh, unprecedented, and surprising places that the digital takes us, so long as those are not placed in direct opposition to the rich past of humanities scholarship that we can draw upon…”. In other words, the digital humanities and the broader spectrum of the humanities may be joined, and data has inherent value, so long as it is evaluated through the critical lens of traditional textual analysis methods, such as careful and
rigorous rereading of texts.

Reflection on the English 203 Course and Conclusion

The fundamental concept that I learned throughout the English 203 course was that nothing is complete at face value—different interpretations exist, and new approaches, such as the use of digital tools, are necessary to furthering understandings of texts, in this case, Hamlet. Critical thinking has been a staple aspect to this course, as, when one is consulting data, they must be aware of what it implies, and how it can be applied to form conclusions. This course has also been instrumental in improving my digital literacy, as I am now able to more readily apply word seer to my research, for near instant results. Additionally, the course has encouraged me to consider the impact of qualitative details within texts more
carefully—ironically, the incorporation of data into my studies of textual analysis has helped me to better understand the importance of words, and how they are dispersed throughout a text(such as the potential significance of the word “disposition” to Hamlet’s behaviour, discussed earlier.) I am now more open-minded in regards to the potential for digital tools and data to supplement closed reading, so long as the two approaches are applied in unison with one another.

To briefly reiterate my argument, based upon the blog post of Michael J. Kramer, and my experiences and work throughout the course, I have concluded that the digital humanities and the humanities, and the quantitative and the qualitative  may blend with one another
to form a cohesive unit, so long as critical thinking is applied to addressing quantitative data that is retrieved using digital humanities approaches.
I then aimed to reaffirm this assertion with a Hamlet case study, a description of word seer’s  potential as a digital tool and its capacity to join the quantitative and the qualitative, and the prospects of the digital humanities and the traditional humanities being
considered as one—similar to the view of Michael J. Kramer, who effectively depicts the relationship as “…not a revolution away from the humanities, but a turn more fully into the humanities.”


Works Cited

Shakespeare, William. Hamlet.  Ann Thompson and Neil Taylor: London, 2006. Print. The Arden Shakespeare Third Series.



Study Break

In light of upcoming exams, I humbly present to you my fellow classmates, a Hamlet inspired study break:

An e-greeting from Polonius

Polonius is offline... Ophelia is offline... Laertes is offline. Gertrude is offline. Claudius is offline. Hamlet is offline. The rest... is silence.

Solid Essay

Simba? Hamlet? MacBeth? You decide.

Director intended. Legit.

Hamlet...what a nut.

Take THAT Claudius

We can all relate to this one


Happy studying, and good luck on exams everyone!

MONK: To be, or not to be?

In all of the discoveries that I have almost made, it seems that MONK has made its decision to ‘not be.’

Unable to create worksets that could be compared for word frequencies, which my group discussed as a good initial focus today, I have found myself at a loss of anything useful to blog about other than how this program has refused to co-operate with me. However, it occurred to me today, that perhaps for the sake of my group I shall force MONK to hand me something useful.

Yes, I do mean force.

In the interest of figuring out what classifies Act V as ‘more tragic’ than Hamlet, I began to use the preset corpus and genre worksets in order to determine which words were frequently used by Shakespeare in his tragedies. The following is what I learned in this endeavour.

It is worth mentioning, I think, for those of you that are familiar with MONK, you know that it has this irritating stubborn thing where it just refuses to remember the options that you have selected to search with when you hit previous, so this process was a long and arduous one.


To begin, I chose the preset worksets to be compared would be all of Shakespeare’s plays with his tragedies, in order to determine which words were unique to his tragedies. I was returned with these:

The words provided in this list are those words that appear most frequently in the comparison between all of Shakespeare’s plays and all of the tragedies.

When I select the word “justify” I am provided with a graph of the frequency of that word across te time span of Shakespeare’s writings:

I found it interesting that the year the word “justify” peaked was roughly around the time when Hamlet was written, and so I hit ‘continue’ in order to see the plays in which this word occurs and in which play in occurred most frequently.

The circulation period I was most interested in was between the year 1600-1610. Finding that time frame on the list, this is what I discovered:

The word ‘justify’ occurs more in Hamlet than it does in any other play in this time period.

It also appears more in Hamlet than it does in any other play, and all the plays on this list in all the time periods, were tragedies.

Going through the list, I found similar words of interest to tragedies (not just in Hamlet). For example, the word ‘rehearse’ appears only, or most frequently in this comparison, in tragedies.

Using words like this, I think it will be of interest to our group in analyzing Act V.


I believe that because Act V was classified by MONK as more tragic that the rest of the play, these words will be helpful in assessing why MONK has made this classification and it will provide a starting point for the other frequency analyzing tools in gathering further interesting analysis about Act V.

I Get By With A Little Help From My Friends- and Monk…

Today was the second meeting with my group on Act 2 and we spent our time trying to figure out ways in which our tool can be helpful to others and ourselves. I am pretty sure that Monk won’t be very helpful with picking up the slack compared to other tools, but I find myself at a large advantage in that every other tool will be helpful to me. Thus I will learn new things about all the other tools, and I can teach my group my frustrations.

I am having difficulty once again just comparing or looking at Act 2 effectively. So I have decided to branch out further and look at Act 2 with much larger parts of Shakespeare mostly focusing on the tragedies.  I have found that common words associated with Act 2 and tragedies such as Richard III, Macbeth and Hamlet as a whole in comparison to Act 2 and its different parts. I have looked at Richard III Macbeth and Hamlet while sifting Hamlet act 2 words throughout it. I can also see where these words are mentioned in comparison to other plays.



The findings show up as the most common seen throughout Shakespeare and then the other two plays as followed. I found that Hamlet is a noun that shows up most often, which does seem obvious since you are comparing words in Hamlet 2.1 to Hamlet itself, these words show up in black. The words which are more commonly seen in other plays than that compared to Hamlet would be seen as an under use in grey.



Some words which I found interesting would be the under use ones. Words such as God and grace appear in such high numbers, but when you look at the comparison between other plays it occurs much more often however, the word heaven can be seen as an overuse word. This is odd because these three words seem connected but yet there is such a strong disconnect between them as an overuse and an underuse. This makes me think once again of what the context of these words could be used, in this case I would like to ask someone in my group who is able to look at these particular words and see who is speaking them, when they are spoken and the context that they are said in. Once again Monk has done a good job at showing you something interesting but it has left it up to you to decide how to handle the information.


I then wondered if these overuse words or underuse words could have been noted in Naye Bayes discussion tree. I decided to look up the underuse words and see if the language could have interpreted it as something with a strong confidence or a weak confidence. At first I looked at God, grace and brother looking at Hamlet, 2.2, and 2.1 as follows. I was surprised to find that a strong confidence showed up for the word Grace in 2.2. I believe this means that the language used in 2.2 can be seen as language which strongly refers grace and other words associated with it. There was also a soft pink shade which with relation to brother and looking at 2.1 which means the language used could be found as a relation to the word brother.



Afterwards I switched to the more common words seen throughout the text and I decided to look at matter, passion and heaven. I found that heaven has a very strong confidence towards 2.1 and matter has a weak relation and passion has no relation.


I find it very odd that some words that were seen as an overuse had such a strong relation to it with words in the text such as grace. As well as words that were commonly found throughout the text shown up as weak, and a common word found such as passion had no reference to the words related within the text.


After my group meeting I meet with my fellow Monk friend Hannah. We compared the ways in which we are trying to be helpful to look at the tool and some issues that have suddenly come up. I know I can speak for the both of us that sometimes the saved worksets that you make won’t let you compare them with other worksets that you have made, it just shows up as a blank. We have tried switching computers, logging off and on, switching internet browsers, making a new project but nothing seems to fix this issue. Although I am happy that it isn’t just me that is having this issue but other Monk individuals as well.


I hope my relation to words within the text will be helpful in my group. I know I will still be dependent on my fellow Monk individuals to help overcome my struggles and see if I am the only ones having these issues or if it others as well. I am very thankful that I am not the only one using Monk and I am not the only one analyzing Act 2. I think for anyone to be effective we have to rely on one another and help others to understand our findings and help push others forward.

I had an epiphany :)

I have finally gained some greater insight to the benefits of text analysis tools. While referring to my first blog post from phase two last week, it was evident that I was struggling with the XML file. I tried again to figure out how Tapor works, but no such luck. So, after devoting hours and developing what feels like carpal tunnel, act 3 is completely hand edited.  Thank God Voyeur can do the rest of my work for me.

Let me say before I begin, that while being in English 205 last semester with professor Ullyot, I read Hamlet for the first time. I gained a surface level understanding. In attempt to analyze the text, In September, we flipped page by page, act by act while attempting to determine if Hamlet really was mad. Talk about old school. It wasn’t until this semester in 203, when I began to deeply analyze Hamlet with the help of Voyeur, that I gained all these great insights into the text. I just think it is amazing how a program is capable of analyzing the text, while bringing words, and other thought provoking ideas to the table. Sorry for the rant, but I am just amazed at my process of learning that these tools have evoked.

Now to be begin..

Act 3 is huge. We have the “to be or not to be” speech, Hamlet and Ophelia explore their relationship, some Guildenstern, Rosencrantz, Claudius, Gertrude, the players and the Mousetrap. In other words a lot of changes are made and a lot of drama begins. My first thought was revenge. Where does revenge appear in act 3? Well apparently not much. A total of 6 different times (revenge, revenged, revengeful). Not all that useful at this point. Today was a day in our meeting, where none of our programs could agree on the amount of times ANY word showed up.  In order to stay consistent, I put my faith in Voyeur.

Moving on, to begin the group focused on analyzing Hamlet and Ophelia’s relationship. There were two reasons for that:

  1. To define their relationship
  2. So we could determine on the same level, what each tool really could add to the analysis

Love was a word that was used 23 times between all of the characters appearing in act three. While concentrating on the scene where Hamlet tells Ophelia to go to a nunnery, Voyeur also picked up on the words honest and fair. However, Hamlet uses these worlds much differently than we do today.

Oxford Dictionary DefinitionÂ

I took the instances where honest and fair appeared and compared them while looking at their context. Since the box inside Voyeur is so tiny, i moved my information to word.

Copy and pasted honesty and fair side by side to compare

It was not until I looked further into Hamlet’s word choices, that I realized how often Hamlet used honest and fair. I have found recently that Hamlet constantly reiterates words as a way to either get answers from someone or to prove a point. Mad/madness is another instance in 3.4, where he keeps hanging on to this idea in order to prove to both his mother and himself that he is not mad. Hamlet’s unwillingness to stop hanging off ideas seems to be one of the biggest give aways to his ‘madness’.

Prior to analyzing Hamlet with the tools, I believed Hamlet had many reasons to act the way he acted. I never wanted to connect his actions to the assumption that he was mad. Again, with the help of the tools, by simply just analyzing Hamlet’s word choices and crazy tangents, its has become more clear than ever that Hamlet is mad. He is always scheming, and diverting his emotions off on to other characters.

Although Hamlet continues to treat Ophelia in a way less than what one would expect, it is interesting to see that Ophelia maintains her respect for him. After Hamlet makes a scene with the honest and fair ordeal, he starts up again and tells Ophelia “I did love you once”. Through the majority of the scene, Ophelia maintains her cool while using God and “sweet heaven” as external powers to ‘help’ Hamlet. Although she is concerned by his actions and words, she never turns on Hamlet or begins to treat him of a lesser value.

In order to further analyze Ophelia and Hamlet’s relationship, the extractor tool from Tapor would be very useful in separating these relationships from the rest of the play.

Life Is Madness

As I do another read through of Act IV of the text of ‘Hamlet’ I find myself with a good couple of pages of notes broken down into what I find interesting or relevant. I know I don’t have everything the text has to offer and so I have produced a few questions in a hope to retrieve some more info.

The part of act IV that catches the most of my interest is the character of Ophelia. It is here where she goes off the deep end, losing herself in madness to go skipping around the castle while singing and passing around dead flowers. I really love this part of the scene because it is so poignant and poetic; I am immediately drawn to the visual and metaphorical niche she hold in regard to nature. In thinking of this I become curious if TAPoR itself is able to pull anything of depth out of what Ophelia does in the act.

At first, the results I pull are a bit disappointing. But then I see the first two frequent words: ‘come’ and ‘gone’. Looking at their context, I see Ophelia uses these words in reference to her father’s death. I think over the connection of the words and I can’t help but think about their reference to life and death. Reading the text, it is clear that Polonius’ death is the reason for Ophelia’s madness, but I come upon the impression that it is also caused by the thought on the futility of life…

Thinking back to 4.3 when Hamlet encounters Fortinbras’ army, I see that this is the answers my question as to why Hamlet is inspired at that moment: Fortinbras is invading Poland for nothing; he is sending his men to die for nothing. Hamlet sees the futility in this and is inspired to do something. TAPoR even demonstrates this answer  in Hamlet’s most frequent words:

Noticing the similarity between Ophelia and Hamlet questioning futility, could it be that ‘madness’ provokes this sort of existential questioning? This is something I may have to return to at a later time.

The main question I pull from Ophelia and her madness is its relation to the supposed madness of Hamlet. It is obvious that Ophelia is much more extreme in what she does. There are similarities I notice between the two, but I still wonder why she is more far gone than Hamlet when they both have the same trigger of death. This thought leads me to question weather Hamlet is genuine in madness, or is putting on an act. I resort to answering this query by searching the word madness and other related references. Here, I find that Ophelia is referred to as mad much more than Hamlet. The references to Ophelia being mad are more to do with her odd actions and speeches, as well as having lost her ‘wits’, where as the only references to Hamlet are in the use of the words ‘mad’ or ‘madness’, despite him having just killed a man…

In my exploration of some of the questions I found while reading, I have found that TAPoR has the ability to make me notice details I hadn’t seen before. In my results, I find a common connection having to do with the states the characters are in in regards to their situations, which just so happens to be the route my group is choosing to go down for our exploration of the act.

Act 4 Thoughts…

The first official group meeting went rather splendid actually. I’m happy to say that I am in a group of keeners and we were all able to communicate our thoughts and expectations clearly. Saying that, the contract was easy to complete as we all wanted the same thing and the best part was that in order to keep everyone motivated on getting their tasks done on time- they would have to buy the rest of the group coffee if they didn’t do their work!


The great part about doing act four is that so much happens in this particular act in the sense that everything from the previous acts are finally tying together leading to the finale of the play. This is where I noticed a lot of character development. Going through the entire play, it’s evident that this happens earlier on, however, in this act you can see whose loyalties lie where and the secret backstairs world of the characters. It’s dirty, revengeful, and full of insanity!

Working with Wordseer, I know I shall have a lot of fun experimenting with what I can find in act four. There are many clues in the language that Shakespeare uses in giving the reader/ viewer an idea of what`s going on, but it will be interesting to see what Wordseer highlights as significant and if it differs from my thoughts or if it`s the same, helping me further analyse the act by certain words.

Something I`m hoping to focus on and find more about is Hamlet`s relationship with his mother, Gertrude. In parts of the play, the reader gets the hint of more than a mother- son relationship, where in this act it completely changes that thought when Gertrude is so eager to rat her son out to Claudius. I`m hoping Wordseer can better help me understand each characters relationship with other characters and who really are friends and foes. I already know this, but perhaps the program will lead me to other clues that might make me think differently.

In act four, scenes five to six, I find it highly amusing when Hamlet taunts Claudius of Polonius`s murder with word games, and saying that he(Polonius) was eaten by worms. This play on different words demonstrates different tones and tact of humor. This is something else that I`m hoping that Wordseer can put light on. The word tree will definitely come in handy as I can see related words which will give me the sense of what else Shakespeare could have meant when he wrote those words.

I`m looking forward to meeting up with my group again and seeing what other interesting things they find with their programs. Also, I`m excited to get in touch with my previous group again to see what Wordseer found for their acts.



A Start on Act 5

Out of all the acts in Hamlet, Act 5 is my favorite.  There is a great philosophical/humorous conversation with some gravediggers to start off the act.  Then, after Hamlet has his famous nostalgic conversation with a skull, there is a dramatic fight between Hamlet and Laertes in the grave of Hamlet’s supposed lover.  But the excitement doesn’t stop there! After an epic sword fight and a bit of poison, the entirety of the royal family ends up dead!!  I think my new button sums up the whole Act nicely.

"Fortinbras should arrive at any moment to turn this mayhem around."

Yet, as it always is with research, the most difficult part in analyzing this Act is figuring out where to start.  The group and I decided to begin by analyzing the Act individually with our respective tools.  The hope is that we will each discover some areas of interest worth collaborating on.

As the TAPoR expert in this group, I know one of the advantages I have is the ability to isolate certain speakers and areas of the play.  Keeping this advantage in mind, I began my analysis by using the List Words tool, as it always offers a good starting point.

List Words results for Act 5

The results of Act 5 did not offer much that I didn’t already know.  Obviously death is a major theme throughout this Act and the King, Hamlet, Laertes and Horatio all major characters associated with it.  The frequency of the word “know” was a bit surprising for me, but further examination with the Concordance tool informed me that it is used within the conversation of Osric, Hamlet and Horatio.  In this case, Hamlet and Horatio are repeating Oseric’s questions as a means to make fun of him.  However, I did notice that this List Words results were a lot different from my results in Act 3.4, where the focus is specifically on Hamlet, Gertrude, and her past relationships. This thought led me to inquire after Hamlet’s change in character throughout the play.  Wanting to explore this inquiry further, I decided to isolate just Hamlet’s lines and again use the List Words tool.  I also did the same with Hamlet’s lines in Act 1 to give myself a comparison point.

Results on Hamlet's lines in Act 5 (right) and his lines in Act 1 (left).

In these results, I was surprised particularly by the comparative frequencies of the word “father.”  In Act 1 it is mentioned 9 times by Hamlet, but in Act 5 in is only mentioned by him once.  I thought this result was interesting because Hamlet’s main motive throughout this act is to avenge his father, but he hardly mentions him in the moments leading up to, and immediately following Claudius’ death.  It seems as though Hamlet Sr. is no longer the main focus of Hamlet’s attentions towards the end of this play.  I do not think his desire for revenge has abated, but when I thought about Hamlet’s motives deeper, I realized that Hamlet kills his Uncle only after the death of Ophelia and his mother.  Perhaps it is this grief combined with Laertes’ confession that finally gives Hamlet the motive to kill Claudius.  This conclusion would then certainly indicate a change in Hamlet’s motive from the beginning to the end of the play.

As I work further with my group, I’m looking forward to seeing how we can expand on each other’s findings.  I believe the most difficult task will be narrowing all our findings into one conclusion, as there is a lot of information at our disposal and a large variety of tools.  It shall be an interesting process.


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.

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’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!


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?

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.

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.

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.

Continue reading

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.

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.

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.

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.

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.