Applying LLM and Topic Modelling in Psychotherapeutic Contexts

TL;DR

This paper demonstrates how Large Language Models and BERTopic can analyze therapist remarks to identify stable topics, offering insights into language patterns in psychotherapy across different styles.

Contribution

It introduces a novel application of BERTopic to psychotherapy dialogue, combining automated topic modeling with expert assessment for improved understanding.

Findings

Identified stable, common topics across different therapeutic styles.

Showed the potential of automated topic modeling to enhance psychotherapy practice.

Provided insights into language patterns in therapy sessions.

Abstract

This study explores the use of Large language models to analyze therapist remarks in a psychotherapeutic setting. The paper focuses on the application of BERTopic, a machine learning-based topic modeling tool, to the dialogue of two different groups of therapists (classical and modern), which makes it possible to identify and describe a set of topics that consistently emerge across these groups. The paper describes in detail the chosen algorithm for BERTopic, which included creating a vector space from a corpus of therapist remarks, reducing its dimensionality, clustering the space, and creating and optimizing topic representation. Along with the automatic topical modeling by the BERTopic, the research involved an expert assessment of the findings and manual topic structure optimization. The topic modeling results highlighted the most common and stable topics in therapists speech, offering insights into how language patterns in therapy develop and remain stable across different therapeutic styles. This work contributes to the growing field of machine learning in psychotherapy by demonstrating the potential of automated methods to improve both the practice and training of therapists. The study highlights the value of topic modeling as a tool for gaining a deeper understanding of therapeutic dialogue and offers new opportunities for improving therapeutic effectiveness and clinical supervision.