使用预训练变换器检测心理健康文本中的情绪漂移
Detecting Emotion Drift in Mental Health Text Using Pre-Trained Transformers
AI总结：
本研究利用预训练变换器模型检测心理健康文本中的情绪变化，通过分析句子层面的情感来识别情绪漂移模式。
摘要：
本研究探讨了情绪漂移：单段文本内心理健康信息中的情感状态变化。尽管情感分析通常将整条信息分类为积极、消极或中性，但信息过程中情感的细微变化往往被忽视。本研究利用预训练变换器模型如DistilBERT和RoBERTa检测句子层面的情感并测量情绪漂移分数。结果提供了对心理健康对话中情绪升调或缓解模式的见解。该方法可以应用于更好地理解内容中的情绪动态。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
