Temporal Topic Analysis with Endogenous and Exogenous Processes
This work addresses the challenge of analyzing time-varying text data in domains like economics and job markets, but it appears incremental as it builds on existing topic modeling approaches.
The authors tackled the problem of modeling temporal textual data influenced by both endogenous and exogenous processes, such as economic fluctuations, by proposing a hierarchical Bayesian topic model that captures relationships between topic distributions and time-dependent factors, and demonstrated its performance on job advertisements and news articles.
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.