Paper: Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students

ACL ID D13-1133
Title Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

We investigate the value-add of topic model- ing in text analysis for depression, and for neu- roticism as a strongly associated personality measure. Using Pennebaker?s Linguistic In- quiry and Word Count (LIWC) lexicon to pro- vide baseline features, we show that straight- forward topic modeling using Latent Dirich- let Allocation (LDA) yields interpretable, psy- chologically relevant ?themes? that add value in prediction of clinical assessments.