Analysis of Mental Health Counseling Conversation Using Natural Language Processing

Abstract

One of the most significant public health challenges of our day is mental illness. Despite the benefits of psychotherapy and counseling, our understanding of conducting effective counseling conversations has been limited due to a lack of high-quality data with labeled results. This research presents a quantitative analysis of relatively good-quality data scraped from an online counseling forum. The dataset comprises questions related to various mental illnesses from actual patients and the responses from professional, certified therapists. Through graphical representations, we visualize the correlation between various linguistic aspects of conversations with conversation outcomes. We further apply certain language models, including the pre-trained BERT model, to analyze the quality of therapist responses. The results are then compared to identify effective conversational strategies contributing to improved outcomes. The novelty of this study lies in the mathematical explanations of Language models, making it a valuable resource for readers seeking a deep understanding of machine learning techniques. Additionally, it provides practical implementation guidance for the BERT model, enhancing its usability in real-world scenarios related to mental health challenges.