Emotions are experienced and expressed through various response systems. Coherence between emotional experience and emotional expression is considered important to clients' well being. To date, emotional coherence (EC) has been studied at a single time point using lab-based tasks with relatively small datasets. No study has examined EC between the subjective experience of emotions and emotion expression in therapy or whether this coherence is associated with clients' well being. Natural language Processing (NLP) approaches have been applied to identify emotions from psychotherapy dialogue, which can be implemented to study emotional processes on a larger scale. However, these methods have yet to be used to study coherence between emotional experience and emotional expression over the course of therapy and whether it relates to clients' well-being. This work presents an end-to-end approach where we use emotion predictions from our transformer based emotion recognition model to study emotional coherence and its diagnostic potential in psychotherapy research. We first employ our transformer based approach on a Hebrew psychotherapy dataset to automatically label clients' emotions at utterance level in psychotherapy dialogues. We subsequently investigate the emotional coherence between clients' self-reported emotional states and our model-based emotion predictions. We also examine the association between emotional coherence and clients' well being. Our findings indicate a significant correlation between clients' self-reported emotions and positive and negative emotions expressed verbally during psychotherapy sessions. Coherence in positive emotions was also highly correlated with clients well-being. These results illustrate how NLP can be applied to identify important emotional processes in psychotherapy to improve diagnosis and treatment for clients suffering from mental-health problems.

本文研究了情感一致性、自然语言处理、心理治疗、情感识别和心理健康等方面，并利用转换器模型从普通话语言处理对话中的客户自我报告的情感状态，分析与客户良好行为之间的情感一致性的关联，为精神健康问题的诊断和治疗提供技术支持。

自然语言处理与心理治疗：使用预测客户情绪和自我报告的客户情绪来衡量情感一致性