Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health

TL;DR

This study employs large-scale NLP techniques to analyze text-message counseling conversations, revealing linguistic strategies linked to improved mental health outcomes and advancing understanding of effective counseling practices.

Contribution

It introduces novel computational methods for analyzing counseling discourse and identifies linguistic features associated with successful therapy outcomes.

Findings

Certain linguistic patterns correlate with better outcomes

Sequence models reveal effective conversation flow

Message clustering identifies successful counseling strategies

Abstract

Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes.