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Topic Modeling and Digital Humanities

Introduction

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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Topic Modeling: A Basic Introduction

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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The Details: Training and Validating Big Models on Big Data

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. This presentation was recorded on November 3, 2012 at the Maryland Institute for Technology as part of the Topic Modeling Workshop, sponsored by the National Endowment of the Humanities and MITH, at the University of Maryland.

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Vol. 2 No. 1 Beginnings Featured Excerpts

“A model of texts, built with a particular theory in mind, cannot provide evidence for the theory.”
David M. Blei

Some of the topics found by analyzing 1.8 million articles from the New York Times

Some of the topics found by analyzing 1.8 million articles from the New York Times

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