Visualizing Topic Models

Authors

  • Allison Chaney Princeton University
  • David Blei Princeton University

Keywords:

Topic Models

Abstract

Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method that learns the underlying themes in a large collection of otherwise unorganized documents. This discovered structure summarizes and organizes the documents. However, topic models are high-level statistical tools—a user must scrutinize numerical distributions to understand and explore their results. In this paper, we present a method for visualizing topic models. Our method creates a navigator of the documents, allowing users to explore the hidden structure that a topic model discovers. These browsing interfaces reveal meaningful patterns in a collection, helping end-users explore and understand its contents in new ways. We provide open source software of our method.

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Published

2021-08-03

How to Cite

Chaney, A., & Blei, D. (2021). Visualizing Topic Models. Proceedings of the International AAAI Conference on Web and Social Media, 6(1), 419-422. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14321