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Detecting Topic Drift with Compound Topic Models

Authors

Dan Knights,Michael Mozer,Nicolas Nicolov

University of Colorado at Boulder,University of Colorado at Boulder,J. D. Power and Associates


Proceedings:

Proceedings of the International AAAI Conference on Web and Social Media, 3

Volume

Issue:

Vol. 3 No. 1 (2009): Third International AAAI Conference on Weblogs and Social Media

Track:

Poster Papers

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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.

DOI:

10.1609/icwsm.v3i1.13982


ICWSM

Vol. 3 No. 1 (2009): Third International AAAI Conference on Weblogs and Social Media