I’ve seen Wordles used before in school projects, but usually for display purposes rather than used as an analytical tool.  Therefore, I was excited to see the application given a new purpose that teachers could easily use in school for a variety of texts.

Word Clouds!

When I imported Project Gutenberg’s text of the first volume of Le Morte D’Arthur into Wordle and Word it Out, these were my results (Sadly, I discovered that the “Loved by the King” font in Wordle was not very, well, kingly, so I switched it to a more appropriate font):

Wordle

Word It Out

It’s not surprising that the most prominent word in both is “Sir”, as most of the characters go by that epithet, nor that “king” and “knight” are also frequently used, emphasizing the courtly genre of the text.  ”CHAPTER” probably is featured since the table of contents was included in my copy and paste, in addition to all the times it is usually used.  I was surprised that Tristram beats out Arthur (in a book titled after him!)  I also found it interesting that words such as “smote”, “battle”, and “slain” are much more prominent than “God” and “worship”, hinting that the divine justification for most of the fighting was not as much of an excuse as it purported to be.

Paraphrasing with Up-Goer Five

Like many of my classmates, I found when I put the top 100 words into Up-Goer Five, that about half the words were not permitted, primarily in the proper name, antiquated term, and knightly terminology categories.  I would doubt the ability of someone to use the Up-Goer Five to summarize books like this with difficult language if I hadn’t seen their application to Hamlet’s “To Be or Not To Be” speech.  (I actually recommended this application to my former co-workers, many of whom require their students to paraphrase the famous soliloquies in Shakespeare’s plays on their tests.)

And I thought I was free from dealing with parts of speech…

I was impressed by the CLAWS Part of Speech Tagger’s ability to correctly identify even the antiquated pronouns such as “ye” and “thee”, but other than that, I found it difficult to see how these kinds of results could be useful in an analysis of the text.  Maybe if there were further calculations applied (frequencies of parts of speech?) I could have seen those patterns to turn into narratives–or at least questions–that Ramsay suggests.

Making some conclusions with TAPoR

When I first plugged the text of Le Morte D’Arthur into TAPoR, the frequency count and “Cirrus” were both dominated by articles and other “unimportant” words, but when I asked the program to remove them, it generated a list almost identical to that of Wordle and Word It Out!  The Word Trends graphs, though, got interesting when I decided to click on those prominent names.

Frequency of Arthur, Tristram, and Launcelot’s appearances in the book

Leaving the “Segments” setting at 10 to roughly mimic the 9 books in Vol. 1, I discovered that Arthur most frequently appears at the beginning of the book (which makes sense, given that it is devoted to the story of how he came to power), and then is practically forgotten about.  Likewise, Tristram dominates the last part of the book, even more so than Arthur.  This makes sense because book 8 is all about Tristram’s adventures.  Similarly, Launcelot spikes in the middle of the graph, as book 6 is all about his deeds.  The juxtaposed graph shows clearly how Malory attempted to integrate all the various legends about the knights which had come from different sources, choosing to do it in an episodic fashion focusing on the character rather than jump back and forth between multiple storylines as is more typical of contemporary literature.

So what is it like to read this?

I think that these activities did have a sense of what Ramsay refers to as  ”ostranenie–the estrangement and defamiliarization of textuality” (3).  However, I’m skeptical as to how far we can take algorithmic analysis when the potential for grasping at straws exists.  As Ramsay mentions later on,

If something is known from a word-frequency list or a data visualization, it is undoubtedly a function of our desire to make sense of what has been presented. We fill in gaps, make connections backward and forward, explain inconsistencies, resolve contradictions, and, above all, generate additional narratives in the form of declarative realizations (62).

How much of this meaning is because we want to see meaning there?  And how much is built on prior assumptions?  For example, am I reading too much into the Word Trend charts of Malory because I know that his project was one of compilation, rather than invention?  I think this gets even trickier when you analyze results of an algorithm that you have designed–your own biases and/or assumptions are built into the project from the start.  Hopefully we’ll talk more in class about when these types of practices are productive and when they produce results that just mirror what we already think.

(And if you’re interested in seeing the outcome of Unicorns vs. Zombies according to Google N-Gram, check out my blog post!)