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Welcome to .txtLAB, a laboratory for cultural analytics at McGill University directed by Andrew Piper. We explore the use of computational and quantitative approaches towards understanding literature and culture in both the past and present. Our aim is to engage in critical and creative uses of the tools of network science, machine learning, or image processing to think about language, literature, and culture at both large and small scale.
Posts tagged "literature"
LIWC for Literature: Releasing Data on 25,000 Documents

LIWC for Literature: Releasing Data on 25,000 Documents

Increasing emphasis is being placed in the humanities on sharing data. Projects like the Open Syllabus Project, for example, have made a tremendous effort in discovering, collecting, and cleaning large amounts of data relevant to humanities research. Much of our data, however, is still locked-up behind copyright and paywalls within university libraries, even when the underlying...
Does the Canon Represent a Sampling Problem? A Two Part Series

Does the Canon Represent a Sampling Problem? A Two Part Series

The most recent pamphlet from the Stanford Literary Lab takes up the question of the representativeness of the literary canon. Is the canon — that reduced subset of literary texts that people actually read long after they have been published — a smaller version of the field of literary production more generally? Or is it substantially different?...
Quantifying the Weepy Bestseller

Quantifying the Weepy Bestseller

I have a new piece out that is appearing in The New Republic. In a number of recent book reviews, literary critics and novelists arrive at the consensus that to be a great writer, one must avoid being “sentimental.” One famous novelist describes it as a “cardinal sin” of writing. But is it actually true? Using a computer science method...