Topic Models for Assessment of Mental Health Issues

Abstract

In this paper, we explore topic modeling for the assessment of risk for depression, anorexia and self-harm. Using social media textual content from different datasets, we focus on Latent Dirichlet Allocation models, trained on both specific and combined corpora made from these datasets to perform risk detection. We investigate mental health vocabulary and shared topic modeling performance improvements on user classification.