  We consider the problem of modeling temporal textual data taking endogenous
and exogenous processes into account. Such text documents arise in real world
applications, including job advertisements and economic news articles, which
are influenced by the fluctuations of the general economy. We propose a
hierarchical Bayesian topic model which imposes a "group-correlated"
hierarchical structure on the evolution of topics over time incorporating both
processes, and show that this model can be estimated from Markov chain Monte
Carlo sampling methods. We further demonstrate that this model captures the
intrinsic relationships between the topic distribution and the time-dependent
factors, and compare its performance with latent Dirichlet allocation (LDA) and
two other related models. The model is applied to two collections of documents
to illustrate its empirical performance: online job advertisements from
DirectEmployers Association and journalists' postings on BusinessInsider.com.
