Text analysis and automatic triage of posts in a mental health forum

Abstract

We present an approach for automatic triage of message posts in ReachOut.com mental health forum, which was a shared task in the 2016 Computational Linguistics and Clinical Psychology (CLPsych). This effort is aimed at providing the trained moderators of ReachOut.com with a systematic triage of forum posts, enabling them to more efficiently support the young users aged 14-25 communicating with each other about their issues. We use different features and classifiers to predict the users' mental health states, marked as green, amber, red, and crisis. Our results show that random forests have significant success over our baseline mutli-class SVM classifier. In addition, we perform feature importance analysis to characterize key features in identification of the critical posts.