🤖 AI Summary
This study leverages natural language processing to quantify emotional dynamics in psychotherapy and examine their association with clinical distress and risk of deterioration. Building upon a Transformer architecture, the authors develop a fine-grained sentiment analysis model to extract utterance- and session-level emotional features from 751 therapy dialogues, which are then statistically linked to OQ-45 scale scores. For the first time, Transformer-derived emotional features are treated as standalone psychometric indicators, revealing significant correlations with the emotion-related subscales of the OQ-45. Moreover, these features exhibit marked differences in patients at high risk of symptom deterioration or premature termination, thereby extending the applicability of artificial intelligence in psychological assessment.
📝 Abstract
Sentiment analysis has been of long-standing interest in psychotherapy research. Recently, the Transformer deep learning architecture has produced text-based sentiment analysis models that are highly accurate and context-aware. These models have been explored as proxies for emotion measurement instruments in psychotherapy, but not investigated as stand-alone psychometric tools. Using proposed utterance-level and session-level sentiment features derived from a fine-grained sentiment model on a large corpus of psychotherapy sessions (N = 751), we investigate the distribution of session aggregated sentiment scores. Further, we characterize the relationship of these features to individual components and the overall score of the OQ-45 instrument and find that this sentiment feature is most strongly correlated to components related to emotional valence in directionally intuitive ways. Finally, we report that there are statistically significant differences between the sentiment distributions for patients flagged as at risk of deterioration or dropping out of care via either the OQ Rational or Empirical outcome models. These correlations to a fully-validated psychometric instrument demonstrate that these proposed sentiment features are, at least, adjunctive measures of client distress and deterioration.