用Transformer分析心理治疗对话，能有效识别患者情绪困扰与恶化风险。
The Association of Transformer-based Sentiment Analysis with Symptom Distress and Deterioration in Routine Psychotherapy Care
- 基于细粒度情感模型提取会话级情感特征
- 情感分数与OQ-45量表中情绪维度高度相关
- 可辅助识别高风险或可能脱落的患者
情感分析在心理治疗研究中长期受关注。近年来，基于Transformer的深度学习模型在文本情感分析上表现出高精度和上下文感知能力。这些模型被探索作为心理治疗中情绪测量工具的替代，但尚未作为独立的心理测量工具被检验。本研究基于751个心理治疗会话的大规模语料库，利用精细情感模型提取话语级和会话级情感特征，分析了会话聚合情感分数的分布。进一步发现，这些情感特征与OQ-45量表的各分量及总分存在关联，尤其与情绪效价相关维度呈现方向性一致的强相关性。最后，结果显示，通过OQ理性或经验模型标记为有恶化或脱落风险的患者，其情感分布具有统计显著差异。这些结果表明，所提出的感情特征至少可作为客户困扰与恶化状态的辅助测量工具。
原文摘要 · Abstract (English)
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.
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