Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy.

Abstract

Objective: Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts.

Method: We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses.

Results: The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination.

Discussion: Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.