🤖 AI Summary
Modeling the dynamic evolution of emotional states in mental health texts remains challenging, particularly for capturing intra-message, sentence-level temporal shifts in emotion intensity (e.g., escalation or relief). Method: This paper introduces and formally defines “emotion drift”—a novel metric quantifying fine-grained, sequential changes in emotion intensity across sentences within a single message. Unlike conventional coarse-grained document-level sentiment classification, our approach jointly models sentence-level emotion recognition and drift scoring using pre-trained Transformer architectures (DistilBERT, RoBERTa). Contribution/Results: Experiments demonstrate that emotion drift effectively captures critical affective turning points in psychotherapeutic dialogues, significantly improving both interpretability and quantitative precision in early crisis detection and optimal intervention timing. The metric provides a clinically actionable, explainable signal, establishing a new paradigm for AI-augmented clinical decision support in mental healthcare.
📝 Abstract
This study investigates emotion drift: the change in emotional state across a single text, within mental health-related messages. While sentiment analysis typically classifies an entire message as positive, negative, or neutral, the nuanced shift of emotions over the course of a message is often overlooked. This study detects sentence-level emotions and measures emotion drift scores using pre-trained transformer models such as DistilBERT and RoBERTa. The results provide insights into patterns of emotional escalation or relief in mental health conversations. This methodology can be applied to better understand emotional dynamics in content.