This is a report on our Text Mining course term project at Praxis Business School, Kolkata 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. We set out to accomplish this goal by performing a topic modeling text analysis on a corpus of 622 key U.S. presidential speeches . The results of the topic modeling analysis of the presidential speeches suggest that the technique accurately identified latent themes or dis- courses across different presidential speeches over time. The results also suggest that it is an effective tool for producing new insights into the history of presidential speeches, including nding similarities between speeches that otherwise might not be apparent.