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.



5 thoughts on “Momentary Panic and Gradual Acceptance

  1. Great post — that’s an inventive solution, and good thinking! Another way to do the same thing would be to use the “Extract Text from XML” tool in TAPoR, which would let you extract (1) all the speech prefixes and (2) all the speeches as _two separate files_. In Phase 2, when you can call on your TAPoR expert, is a good time to do stuff like this.

  2. My initial thoughts about this course were similar to yours. I too am trying to distance myself from the “cookie cutter approach” to English that high school teaches.

    I’m in the TAPoR team and there us a similar tool to Word Trends in TAPoR that lists the frequency of words in a text (I believe it is called List Words). However, it does not show where they appear in the text like Voyeur’s Word Trends tool does. I personally think that the Voyeur tool is more effective in this way because it allows you to identify where a particular theme, or in this case, word, is more concentrated in the text.

    Also, thank you for posting that a copy of the XML file you made to the blog. Our team has been using it as well in order to avoid the same problem you were having.

  3. Katy:

    I agree with your point about the “cookie-cutter” method of teaching when it comes to English. It’s so refreshing to use a “digital approach” to text especially for someone who thinks text analysis is a tedious and painful process. After fiddling around with the various tools, including ours [Voyeur], I’ve started to think about how these tools will improve my understanding of the text. Many of us, including myself, have read Hamlet or know a thing or two about the play from popular culture. Surprisingly, these tools are helping me discover parts of the text I hadn’t realized before. Like the lack of care for Polonius when he is stabbed in 3.4 (both Hamlet and Gertrude continue discussing mother-son affairs) or the amount of incest that exists in this scene and in other parts of the text.

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