at the Harvard Metalab:
metalab.harvard.edu/2012/07/paper-machines/

"Below is a screenshot from the “topic” view of our application, showing the
output of a topic model analysis. Such an analysis assumes that texts are
comprised of various collections of words (“topics”) according to a probability
distribution; the algorithm infers which words make up which topics and which
topics make up which documents based solely on word co-occurrence, without prior
semantic knowledge. These topics are machine-generated, but often reveal a
surprising coherence; for example, the “estate, tenant, law” topic above also
includes “property, right, rent, lease, heir, inclosure” and so on. The topics
below give a sense of some of the changing concerns in two subsets of the
corpus:"

"My own research as a Ph.D student at Brown focuses on the intertwining of
technology with race, gender, and sexuality in contemporary African-American
music and online discourse on sites like Twitter, Tumblr, Okayplayer and
Rapgenius. While my subject is superficially quite different from Jo’s, I
believe this tool will help me to condense the massive corpora of these online
fora into more manageable forms. I am excited to see what other uses may emerge
from this project once it is released to the broader community."