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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Topic Detection and Tracking - Phase 4, http://projects.ldc.upenn.edu/TDT4/
Allan, J., Lavrenko, V., Nallapati., R.: UMass at TDT 2002. In: Notebook Proceedings of TDT 2002 Workshop (2002)
Asuncion, A., Welling, M., Smyth, P., Teh., Y.W.: On Smoothing and Inference for Topic Models. In: UAI 2009 (2009)
Blei, D.M., Lafferty, J.D.: Dynamic Topic Models. In: Airoldi, E.M., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds.) ICML 2006. LNCS, vol. 4503, pp. 113–120. Springer, Heidelberg (2007)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. JMLR 3, 993–1022 (2003)
Cabilio, P., Masaro, J.: Basic Statistical Procedures and Tables. Department of Mathematics and Statistics, Acadia University (2005)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. of Natl. Acad. Sci. 101(suppl.1), 5228–5235 (2004)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Minka, T.: Estimating a Dirichlet distribution (2000), http://research.microsoft.com/~minka/papers/dirichlet/
Nallapati, R.M., Ditmore, S., Lafferty, J.D., Ung, K.: Multiscale Topic Tomography. In: KDD 2007, pp. 520–529 (2007)
Ren, L., Dunson, D.B., Carin, L.: The Dynamic Hierarchical Dirichlet Process. In: ICML 2008, pp. 824–831 (2008)
Shah, C., Croft, W.B., Jensen, D.: Representing Documents with Named Entities for Story Link Detection. In: CIKM 2006, pp. 868–869 (2006)
Masada, T., Fukagawa, D., Takasu, A., Hamada, T., Shibata, Y., Oguri, K.: Dynamic Hyperparameter Optimization for Bayesian Topical Trend Analysis. In: CIKM 2009, pp. 1831–1834 (2009)
Wang, C., Blei, D., Heckerman, D.: Continuous Time Dynamic Topic Models. In: UAI 2008, pp. 579–586 (2008)
Wang, X.-R., McCallum, A.: Topics over Time: A Non-Markov Continuous-time Model of Topical Trends. In: KDD 2006, pp. 424–433 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Masada, T., Fukagawa, D., Takasu, A., Shibata, Y., Oguri, K. (2010). Modeling Topical Trends over Continuous Time with Priors. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)