Dynamic hyperparameter optimization for bayesian topical trend analysis

Authors:
Tomonari Masada;Daiji Fukagawa;Atsuhiro Takasu;Tsuyoshi Hamada;Yuichiro Shibata;Kiyoshi Oguri
Affiliations:
Nagasaki University, Nagasaki, Japan;National Institute of Informatics, Tokyo, Japan;National Institute of Informatics, Tokyo, Japan;Nagasaki University, Nagasaki, Japan;Nagasaki University, Nagasaki, Japan;Nagasaki University, Nagasaki, Japan
Venue:
Proceedings of the 18th ACM conference on Information and knowledge management
Year:
2009

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Abstract

This paper presents a new Bayesian topical trend analysis. We regard the parameters of topic Dirichlet priors in latent Dirichlet allocation as a function of document timestamps and optimize the parameters by a gradient-based algorithm. Since our method gives similar hyperparameters to the documents having similar timestamps, topic assignment in collapsed Gibbs sampling is affected by timestamp similarities. We compute TFIDF-based document similarities by using a result of collapsed Gibbs sampling and evaluate our proposal by link detection task of Topic Detection and Tracking.