Modeling topical trends over continuous time with priors

Authors:
Tomonari Masada;Daiji Fukagawa;Atsuhiro Takasu;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
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

Citing 9
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Abstract

In this paper, we propose a new method for topical trend analysis We model topical trends by per-topic Beta distributions as in Topics over Time (TOT), proposed as an extension of latent Dirichlet allocation (LDA) However, TOT is likely to overfit to timestamp data in extracting latent topics Therefore, we apply prior distributions to Beta distributions in TOT Since Beta distribution has no conjugate prior, we devise a trick, where we set one among the two parameters of each per-topic Beta distribution to one based on a Bernoulli trial and apply Gamma distribution as a conjugate prior Consequently, we can marginalize out the parameters of Beta distributions and thus treat timestamp data in a Bayesian fashion In the evaluation experiment, we compare our method with LDA and TOT in link detection task on TDT4 dataset We use word predictive probabilities as term weights and estimate document similarities by using those weights in a TFIDF-like scheme The results show that our method achieves a moderate fitting to timestamp data.