用预训练模型捕捉心理健康文本中的情绪变化，揭示情感波动模式。
Detecting Emotion Drift in Mental Health Text Using Pre-Trained Transformers
- 用DistilBERT和RoBERTa检测句子级情绪，计算情绪漂移分数。
- 发现心理对话中存在情感升级或缓解的明显模式。
- 适合研究情绪动态、心理对话分析的学者与应用开发者。
本研究探讨情绪漂移：在单个心理健康相关文本中情绪状态的变化。传统情感分析通常将整条消息分类为正面、负面或中性，但忽略了消息内部情绪的细微演变。本研究利用DistilBERT和RoBERTa等预训练变换器模型，检测句子级情绪并计算情绪漂移得分，揭示了心理对话中情感升级或缓解的规律。该方法可帮助更深入理解内容中的情感动态。
原文摘要 · Abstract (English)
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.
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