Detecting Topic Drift with Compound Topic Models

Authors

  • Dan Knights University of Colorado at Boulder
  • Michael Mozer University of Colorado at Boulder
  • Nicolas Nicolov J. D. Power and Associates

Keywords:

Topic models, Topic tracking, Topic drift, Trend identification and tracking

Abstract

The Latent Dirichlet Allocation topic model of Blei, Ng, and Jordan (2003) is well-established as an effective approach to recovering meaningful topics of conversation from a set of documents. However, a useful analysis of user-generated content is concerned not only with the recovery of topics from a static data set, but with the evolution of topics over time. We employ a compound topic model (CTM) to track topics across two distinct data sets (i.e. past and present) and to visualize trends in topics over time; we evaluate several metrics for detecting a change in the distribution of topics within a time-window; and we illustrate how our approach discovers emerging conversation topics related to current events in real data sets.

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Published

2009-03-20

How to Cite

Knights, D., Mozer, M., & Nicolov, N. (2009). Detecting Topic Drift with Compound Topic Models. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 242-245. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/13982