The associated Python script is for use with my course term project (IS590 Introduction to Digital Scholarship at the University of Tennessee) to assess the utility of a computational analytic technique called probabilistic topic modeling to identify latent topics or themes present in a large corpus of textual information. I set out to accomplish this goal by performing a topic modeling text analysis on a corpus of 622 key U.S. presidential speeches identified by the University of Virginia Miller Center and archived on their web site at http://millercenter.org/president/speeches.

The Python script uses the gensim topic modeling toolkit. More on this project, including the results, original speeches, and output logs, can be found on figshare at: https://dx.doi.org/10.6084/m9.figshare.2060724