Leveraging natural language processing to study emotional coherence in psychotherapy.

Abstract

The association between emotional experience and expression, known as emotional coherence, is considered important for individual functioning. Recent advances in natural language processing (NLP) make it possible to automatically recognize verbally expressed emotions in psychotherapy dialogues and to explore emotional coherence with larger samples and finer granularity than previously. The present study used state-of-the-art emotion recognition models to automatically label clients’ emotions at the utterance level, employed these labeled data to examine the coherence between verbally expressed emotions and self-reported emotions, and examined the associations between emotional coherence and clients’ improvement in functioning throughout treatment. The data comprised 872 transcribed sessions from 68 clients. Clients self-reported their functioning before each session and their emotions after each. A subsample of 196 sessions were manually coded. A transformer-based approach was used to automatically label the remaining data for a total of 139,061 utterances. Multilevel modeling was used to assess emotional coherence and determine whether it was associated with changes in clients’ functioning throughout treatment. The emotion recognition model demonstrated moderate performance. The findings indicated a significant association between verbally expressed emotions and self-reported emotions. Coherence in clients’ negative emotions was associated with improvement in functioning. The results suggest an association between clients’ subjective experience and their verbal expression of emotions and underscore the importance of this coherence to functioning. NLP may uncover crucial emotional processes in psychotherapy.