Detecting Emotion Drift in Mental Health Text Using Pre-Trained Transformers
This addresses the need for nuanced emotional analysis in mental health conversations, though it is incremental as it applies existing transformer methods to a specific domain.
This study tackled the problem of detecting emotion drift, or changes in emotional state across a single text, within mental health messages by using pre-trained transformer models like DistilBERT and RoBERTa to measure sentence-level emotions and drift scores, providing insights into patterns of emotional escalation or relief.
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.