Topic modeling provides a suite of algorithms to discover hidden thematic structure in large collections of texts. The results of topic modeling algorithms can be used to summarize, visualize, explore, and theorize about a corpus.
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The purpose of this post is to help explain some of the basic concepts of topic modeling, introduce some topic modeling tools, and point out some other posts on topic modeling.
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In this video, David Mimno discusses some of the different choices one can make in training models and what their implications are for efficiency, scalability, and topic quality, using the MALLET topic modeling package.
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“A model of texts, built with a particular theory in mind, cannot provide evidence for the theory.”
Some of the topics found by analyzing 1.8 million articles from the New York Times
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