Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents

Iulian Pruteanu-Malinici, Lu Ren, John Paisley, Eric Wang, Lawrence Carin
2010 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We consider the problem of inferring and modeling topics in a sequence of documents with known publication dates. The documents at a given time are each characterized by a topic, and the topics are drawn from a mixture model. The proposed model infers the change in the topic mixture weights as a function of time. The details of this general framework may take different forms, depending on the specifics of the model. For the examples considered here we examine base measures based on independent
more » ... sed on independent multinomial-Dirichlet measures for representation of topic-dependent word counts. The form of the hierarchical model allows efficient variational Bayesian (VB) inference, of interest for large-scale problems. We demonstrate results and make comparisons to the model when the dynamic character is removed, and also compare to latent Dirichlet allocation (LDA) and topics over time (TOT). We consider a database of NIPS papers as well as the United States presidential State of the Union addresses from 1790 to 2008.
doi:10.1109/tpami.2009.125 pmid:20431127 fatcat:z46bilioefbihj2z6gxqrexgku