Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students
نویسندگان
چکیده
We investigate the value-add of topic modeling in text analysis for depression, and for neuroticism as a strongly associated personality measure. Using Pennebaker’s Linguistic Inquiry and Word Count (LIWC) lexicon to provide baseline features, we show that straightforward topic modeling using Latent Dirichlet Allocation (LDA) yields interpretable, psychologically relevant “themes” that add value in prediction of clinical assessments.
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