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Big Modernism Goes Macro

Work on Big Modernism goes macro! In the past year, with the Modernist Versions Project and Compute Canada, we’ve been expanding the plain-text repository of modernist prose from thirty-two to eighty-six texts. We’ve added texts by key authors (Katherine Mansfield, Dorothy Richardson, and John Steinbeck, among others), as well as additional texts by the authors we had already included. While still unable to access many modernist texts online (I’ve written about this here), we are now able to experiment on a bigger canvas of literary modernism.

Using scripts that incorporate topic modelling software and Bayesian analysis algorithms, Compute Canada researcher Belaid Moa and I have constructed a multidimensional space in which to better understand the intricate relationships among the novels in our corpus. The scripts position texts according to their topical relevance, as I’ve explained in a previous post. With a larger data set, however, different patterns emerge (see the raw comparison data here). Read more

Vizualizing Communities in Mrs. Dalloway

“It was precisely twelve o’clock; twelve by Big Ben; whose stroke was wafted over the northern part of London; blent with that of other clocks, mixed in a thin ethereal way with the clouds and wisps of smoke, and died up there among the seagulls–twelve o’clock struck as Clarissa Dalloway laid her green dress on her bed, and the Warren Smiths walked down Harley Street. Twelve was the hour of their appointment. Probably, Rezia thought, that was Sir William Bradshaw’s house with the grey motor car in front of it. The leaden circles dissolved in the air.”

According to network analysis, paragraph 349 in Mrs. Dalloway is the most central; that is, in the whole of the novel, this is the paragraph that connects the greatest number of significant character nodes. That it takes place in the middle of the day seems to indicate the extent of Woolf’s, perhaps unconscious, narrative ability. Read more

Presenting Big Modernism

These are the slides from a Brown Bag Lecture given by Belaid Moa (Compute Canada) and myself at the University of Victoria, April 2014. In the presentation we addressed computer-assisted methods for analyzing modernist literature as Big Data and revealed our preliminary results. For a longer blog post see Making Models of Modernism.

Making Models of Modernism

Distribution of Modernist Topics
Distribution of Modernist Topics

This semester, with the Modernist Versions Project and the Maker Lab in the Humanities, Belaid Moa (Compute Canada) and I have been topic modelling modernist texts. In doing this work, we are hoping to identify heretofore unidentified patterns, both thematic and stylistic, across a (for now, admittedly small) corpus of modernist texts.

Topic modelling assumes authors create documents using collocated clusters of words. By working “backward,” computer algorithms sort the words from a set of pre-processed documents and generate lists of words that comprise these clusters. In our work, we are using the LDA (Latent Dirichlet Allocation) probabilistic model. This popular model operates on the Bayesian method of inference, a mathematical concept that works backward from an observed set of data to calculate the probability of certain conditions being in place in order to produce that set of data. In other words, it depends on a notion of causality and asks what circumstances need to be in place in order for certain results to occur. Read more

Making Modernism Big

This semester, with the Modernist Versions Project (MVP) and Maker Lab in the Humanities, I have been creating a repository of modernist texts for the purposes of text analysis and machine learning. The scope of this project requires a powerful infrastructure, including hardware, software, and technical support, provided in part by Compute Canada, a high performance computing resource platform for universities and institutions across Canada. Last semester was spent aggregating a significant number of modernist texts (in TXT format) and learning the affordances of computer vision. The goal is to mobilize machine learning techniques to infer as yet unseen patterns across modernism. We hope that scripts written in collaboration with Compute Canada will allow us to be comprehensive and equitable in our articulation of modernism.

Read more

Michael Stevens to Moderate Third YoU Twitter Chat

On Friday, August 3rd at 2 pm EDT / 11 am PDT, Michael Stevens will moderate the MVP’s third YoU Twitter chat, focusing specifically on “Lotus Eaters.” The hashtag for the Twitter chat is #yearofulysses.

Michael Sevens is a MA candidate in English at the University of Victoria and has also done postgraduate work at Trinity College, Dublin. He is presently working on a mobile app for navigating Joyce’s Dublin, one that attempts to bring together the heterotopic reality of Joyce’s imagined Dublin together with contemporary reality.

email: | twitter: @mchlstvns