Group Members:
Kristen Gray
Charity Hancock
Daniel Kason
Kathryn Skutlin
Allison Wyss

The Data Analysis Group of Professor Fraistat’s ENGL 738T seminar set out to use the visualization and analysis tool Woodchipper with the goal of finding patterns among a collection of texts.  We chose the “Gothic” genre and further limited the texts to the 18th and 19th centuries—beginning with arguably the first Gothic novel, Horace Walpole’s The Castle of Otranto, and ending with Bram Stoker’s Dracula.  The initial thirty Gothic texts chosen consist of some of the most renowned titles in Gothic literature, including The Mysteries of Udolpho by Ann Radcliffe, The Monk by Matthew Gregory Lewis, Frankenstein by Mary Shelley, and works by the Brontë sisters and Edgar Allan Poe. A complete spreadsheet of the texts can be found here.  In order to further test our research, outliers were also added. The list of non-Gothic texts included Great Expectations by Charles Dickens, Barchester Towers by Anthony Trollope, Treasure Island by Robert Louis Stevenson, and slave narratives by Mary Prince and Frederick Douglass. The digital versions of the texts were found through Project Gutenberg and Hathi Trust. MITH’s Travis Brown used these texts to generate a list of topic models, shown here.

While Gothic is an established genre, we found “the Gothic” an intriguingly difficult term to define.  We agreed that there are similar Gothic traits (horror, the supernatural, mood, etc.), but not all appear in every text. We also found that there was the potential that the genre evolved over time or could be further divided into subsets of the genre. These recognitions led us to several curious research questions: Can we identify through data mining the general Gothic elements? Which texts adhere? Which break away? Why? Do some passages register as “most” or “least” Gothic? Working with students from the University of Virginia, we hoped to answer these questions by generating interesting results. When we met as a group and perused our list of texts, we selected several themes we thought might be promising to investigate using Woodchipper. The topics we decided upon were as follows: chronology, genre deviations, geography/setting, and gender.

One subgroup approached their topic of geography from a more exploratory perspective, applying external constraints to text choices (known setting of novel) while remaining open to the direction Woodchipper’s results would take them. Likewise, another subgroup chose texts based around their overarching topic of chronology and were initially able to accommodate the wide-ranging results they encountered. However, when they began to look for specific deviations from the Gothic genre (the delineation of Science Fiction and Horror), the topics returned by Woodchipper did not always seem relevant to their investigation. The subgroup interested in gender (1) (2), who followed a more categorization-based approach, also experienced the same conundrum. They started out with clear definitions of the male and female Gothic, which they hoped Woodchipper would help them interrogate. While they were able to discern some alignment between the results and their guiding hypothesis (the limiting and faulty perspective of male and female Gothic subgenres), many of the topics that governed the resulting splash patterns, though compelling in and of themselves, did not directly aid their inquiry.

For those groups that ran multiple texts for cross-examination, an additional feature of layering in Woodchipper would have been helpful. When they ran multiple works, there were often disproportionate data point clusters across the texts, which made it impossible to view the data point details on the bottom layers:

With a bit of reconfiguring, they were able to strategically list their texts pre-run so that higher volume works appeared in the bottommost layers; however, they were still unable to access those submerged data points. To reverse the process in order to do so was time-consuming. One group member in particular decided to run two texts separately in order to bypass this issue, though such an approach underutilized Woodchipper’s large-scale text-mining abilities:

We were surprised in our first post-Woodchipper run meeting to find that we were all using such divergent methods, which gave us a range of experiences to discuss. One group member could give advice to another, and each subgroup could return to the project with new ideas for fresh approaches—different combinations of texts to run, different methods for looking at splash patterns, and new ways to understand the topics.  After multiple runs, additional texts were added to the original list to further solve (or complicate) our findings.

Though our team included five students studying at UMD and two students from UVA, cross-campus collaboration worked well within the project model we established. With everyone running their own data, then meeting to discuss the findings, there were few scheduling hiccups. Subgroups consisting of two to three members had an easy enough time communicating electronically, rather than face to face. Moreover, it proved fruitful even when a remote group member digressed from the assigned topic, because the resulting analysis was unexpected and showed us the strengths and weakness of yet another way to use Woodchipper.

Labeling topics was a particularly frustrating but ultimately fascinating aspect of the project. Regarding one recurring set in particular (“Felt,” “Made,” “Conduct,” “Received,” “Heart”), each of us offered a varying interpretation in our individual projects. Not surprisingly, our different labels led us to view the category differently and reach alternate interpretations of the data. It was only through collaboration that we became aware of the abundance of differing options and were thus wary of hastily adhering to the first or easiest one.

The biggest challenge was melding our findings into one cohesive conclusion. While each of our individual methodologies matured as a result of discussion, they never merged into one unified method. By the end of the project, we could agree on a very general set of “good idea” practices, but no steadfast rules for processing texts and no overarching procedures for understanding them. Thus, we could compare our results tentatively, perhaps interpretively, but certainly not conclusively. Different ideas went into the chipper in different ways and through different methods—could we be disappointed not to end up with a grand and unifying conclusion about the Gothic novel? Well, yeah, some of us were disappointed.

Ultimately, we decided it was better that way. Instead of conclusions, we found more questions. Each person’s approach led to a different way of seeing the data. The collaborative approach, like Woodchipper, is effective for inspiring new and deeper ways of thinking about literature. However, the individual must be capable of choosing one approach and following it to a conclusive critical end. This parallels all the work we did with Woodchipper. While Woodchipper might be able to more definitively “prove” a trend with a much larger data set, our limited number of texts only allowed it to act as a tool for generating ideas. Interpreting the data, then following it up with old-fashioned textual analysis, falls to the human user.

Location, location, location!

What is the “Gothic”?  We came up with several terms that seem to come to mind when one thinks of a Gothic novel.  One of the more compelling aspects was finding texts to help complete our list of both Gothic and non-Gothic novels. Another interesting component to this project is working with our sister class from UVA.  Being that this is a technoromantic class, incorporating technology in order to keep in contact was definitely a must.  We made use of phone, email, googledocs, as well as skype.

For our mini sub-group, Lingerr and I examined how location/setting helped to define Gothic novels.  This topic explores both country of origin (these novels span from England to France to Switzerland…) as well as the physical location of some of the settings (who doesn’t like a castle and a creepy manor?)  The goal was to (hopefully) find a link or at least a similarity between texts that took place within the same country as well as physical setting.  Much to my delight, many of the runs did find such congruities…

This run uses The Mystery of Edwin Drood, Dracula, and Great Expectations, which are novels that take place in England.  There are clear similarities of plotting among the topics (“facial features”, “time”, etc.).  Interestingly enough, the non-Gothic outlier I threw in, Great Expectations, fits in almost perfectly.  This interesting finding slightly complicates whether the topic models are helping me to define the Gothic or to find similarities amongst geography…

Here is a run where Jane Eyre, another England Gothic novel, is thrown into the mix.  Once again, there are many similarities (with Jane Eyre plotting farther down the topic of “love”).

London was a bit of a different story.

This is a run of London Gothic novels, The Beetle, The Picture of Dorian Gray, and The Vampyre.  The Beetle and Dorian Gray have some similar plotting along “inquiry” and “appearance”, but The Beetle also diverges a bit.  The Vampyre doesn’t follow the similar pattern of the other two texts and positions itself in the interior of the graph.

Varney, The Vampyre (which contains London as one of its settings) is added.  Some of the topic models change, and while there are still paths of similar plotting, there is far more incongruity amongst the texts in the middle.

This run explored Gothic novels that took place in large manors.  There is similar plotting, and the topic models focus on “room, time, found…” and “day, time, home…”  The three authors are British (the Bronte sisters) though Vilette takes place in Belgium.  Interestingly, one could argue that there is harmony here because of the locations or because the sisters may have similar writing styles…

This run compares the English manors of Wuthering Heights to the French cathedral of The Hunchback of Notre Dame.  The only similar plotting here is along the topic “stone, great, feet, walls.”

All in all, Woodchipper is a fascinating and useful tool.  The above is just a small portion of the results of the runs I’ve done through Woodchipper.  I wanted to present findings in which the tool was able to both prove and disprove my optimistic hypothesis.  One of the frustrations of the tool is that it picks the most popular topics that appear whereas it might be useful to the user to be able to pick similar topics throughout all of the runs in order to make more conclusive findings.  Location topic models appear, but they don’t necessarily show similarities within the actual plot of the stories.  Perhaps I’m glad that Woodchipper isn’t able to make conclusive findings on its own 100% of the time.  I don’t think I’m ready to take out the human agency in exploring and classifying literature yet.

Well, fears of computers (A.I.) taking over and humans being obsolete may have been brought into fruition.  A global advertising firm has tried to be a trendsetter and used homeless people as roving Wi-Fi hotspots…

Frankenstein and the Female

The film really uplays the role of women to the plot.  One of the major film additions was that Victor Frankenstein chose to reanimate the recently slain Elizabeth.  While he viewed his original creation as an abomination, he chose to forgo all of his “morals” and resurrect his wife.  It raises the interesting question, what is the difference?  Is it because he loved Elizabeth that it would be alright for her to be brought back to life, and thus she would be a ‘good monster’?  If he could give her a chance, why was he able to give up so quickly on his own monster?  Is it that the monster was a collection of random flesh?  The monster (in the film) did, after all, contain his deceased mentor’s brain.  Would he not retain some of those memories and recognition? This scene offers an interesting addition to the plot.  Frankenstein and his monster are now fighting for the affections of the same woman.  Victor wants her to cling on to humanity, the monster wants her to embrace monstrosity.  It is both a philosophical and physical tug of war.

In the end Elizabeth resolves the issue herself; she realizes what she is and destroys herself.

I felt the movie did a real disservice in downplaying the scene in which Victor reneges on creating a mate for his monster.  There is no reflection on the fate of the world and future generations.  There is no consideration to the possibility of a female monster’s possible rejection of her mate or potential for procreation.  There isn’t the episode where Victor gets halfway through creating the female, sees the morbid delight in his first monster’s eyes, and then chooses to destroy it right in front of him.  The monster’s agony is of key importance in the novel, and it lends more weight to his threat of being with Victor on his wedding night.  The film chooses to have Victor object to making a female monster because he can’t stand the idea of using Justine’s body.  The monster’s threat that follows, while he does stay true to his word, somehow doesn’t seem to have as much of an impact.  I will recognize, however, that the change, the monster choosing Justine’s body, is an interesting one.  Victor’s scientific method used random body parts, largely from people who were strangers to him.  But now he is presented with this pretty young thing that he has known all his life.  Is his refusal because he knows her or is it because he has never had to “dissect” a female before.  The monster even taunts him with the notion that it is just raw material.  I wonder if the monster chose this body in order to torment Victor or because he truly found her beautiful.  Victor doesn’t voice the reason behind his refusal, but the understanding should be that it is because he knew her.  But what is the full extent of the refusal.  Is it that (as I mentioned above) that he couldn’t stand the fact of working on her?  If reanimated, would she curse him for her existence?  Could he not stomach the idea of his monster having his way with this once lovely girl?  Whatever the reason, the audience is not let into the inner workings of Victor Frankenstein’s mind, and the monster doesn’t seem to lament the decision like he did in the novel.

A key depiction of the monster in the film is that he is somewhat lustful.  While the novel monster is a lonely outcast looking for companionship and understanding, the film monster is very touchy-feely with the females he comes across.  When he encounters Justine in the film, he waves his hand over her as if longing to touch, and the sound of dogs and searchers in the background interrupt him from whatever lengths he was preparing himself to do.

Additionally, it is Justine who he later chooses to be his bride.  In the book, while he does stare at her and notice her beauty, he is more angered than anything, knowing that someone like her could never want him.  He frames her and moves on.  When it comes to Elizabeth, in the novel the reader is only given two screams and the monster escapes.  In the film, the monster lies on top of Elizabeth (he even tells her not to scream), a position Victor was in only a few moments before.

The monster stares longingly at her for a considerable time and even compliments her beauty.  Once again, loud noise in the background interrupts him from whatever else he might have done, and he kills her right in front of Victor.  He vies for her affection when Victor revives her, but like Frankenstein pondered in the novel, she rejects him.  Love and lust is never fully reciprocated for any of the characters.  The females of Frankenstein are destroyed, and the males continue the rest of their lives as wretches.