  As electronically stored data grow in daily life, obtaining novel and
relevant information becomes challenging in text mining. Thus people have
sought statistical methods based on term frequency, matrix algebra, or topic
modeling for text mining. Popular topic models have centered on one single text
collection, which is deficient for comparative text analyses. We consider a
setting where one can partition the corpus into subcollections. Each
subcollection shares a common set of topics, but there exists relative
variation in topic proportions among collections. Including any prior knowledge
about the corpus (e.g. organization structure), we propose the compound latent
Dirichlet allocation (cLDA) model, improving on previous work, encouraging
generalizability, and depending less on user-input parameters. To identify the
parameters of interest in cLDA, we study Markov chain Monte Carlo (MCMC) and
variational inference approaches extensively, and suggest an efficient MCMC
method. We evaluate cLDA qualitatively and quantitatively using both synthetic
and real-world corpora. The usability study on some real-world corpora
illustrates the superiority of cLDA to explore the underlying topics
automatically but also model their connections and variations across multiple
collections.
