Analyses of Multi-collection Corpora via Compound Topic Modeling

Abstract

Popular probabilistic topic models have typically centered on one single text collection, which is deficient for comparative text analyses. We consider a setting where we have partitionable corpora. Each subcollection shares a single set of topics, but there exists relative variation in topic proportions among collections. We propose the compound latent Dirichlet allocation (cLDA) model that encourages generalizability, depends less on user-input parameters, and includes any prior knowledge corpus organization structure. For parameter estimation, we study Markov chain Monte Carlo (MCMC) and variational inference approaches extensively and suggest an efficient MCMC method. We evaluate cLDA using both synthetic and real-world corpora and cLDA shows superior performance over the state-of-the-art models.