Temporal Topic Analysis with Endogenous and Exogenous Processes

TL;DR

This paper introduces a hierarchical Bayesian model for analyzing temporal textual data that accounts for both endogenous and exogenous influences, improving understanding of how external factors like economic fluctuations affect topic evolution.

Contribution

The paper proposes a novel hierarchical Bayesian topic model that captures the influence of external economic factors on topic dynamics over time, estimated via MCMC methods.

Findings

Model effectively captures relationships between topics and external factors.

Outperforms LDA and related models in empirical tests.

Applied successfully to job ads and news articles.

Abstract

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.