Emotional distress is a common reason for seeking psychotherapy, and sharing emotional material is central to the process of psychotherapy. However, systematic research examining patterns of emotional exchange that occur during psychotherapy sessions is often limited in scale. Traditional methods for identifying emotion in psychotherapy rely on labor-intensive observer ratings, client or therapist ratings obtained before or after sessions, or involve manually extracting ratings of emotion from session transcripts using dictionaries of positive and negative words that do not take the context of a sentence into account. However, recent advances in technology in the area of machine learning algorithms, in particular natural language processing, have made it possible for mental health researchers to identify sentiment, or emotion, in therapist–client interactions on a large scale that would be unattainable with more traditional methods. As an attempt to extend prior findings from Tanana et al. (2016), we compared their previous sentiment model with a common dictionary-based psychotherapy model, LIWC, and a new NLP model, BERT. We used the human ratings from a database of 97,497 utterances from psychotherapy to train the BERT model. Our findings revealed that the unigram sentiment model (kappa = 0.31) outperformed LIWC (kappa = 0.25), and ultimately BERT outperformed both models (kappa = 0.48).

情绪困扰是寻求心理治疗的常见原因，分享情绪材料是心理治疗过程的核心。然而，检查心理治疗期间发生的情绪交流模式的系统研究通常规模有限。在心理治疗中识别情绪的传统方法依赖于劳动密集型的观察者评级、在会议之前或之后获得的客户或治疗师评级，或者涉及使用不带上下文上下文的积极和消极词的字典从会议记录中手动提取情绪评级考虑到句子。然而，机器学习算法领域的最新技术，特别是自然语言处理，使心理健康研究人员能够识别情绪或情绪，在治疗师与客户的大规模互动中，这是使用更传统的方法无法实现的。作为扩展 Tanana 等人先前发现的尝试。（2016 年），我们将他们之前的情感模型与基于字典的常见心理治疗模型 LIWC 和新的 NLP 模型 BERT 进行了比较。我们使用来自心理治疗的 97,497 个话语的数据库中的人类评分来训练 BERT 模型。我们的研究结果表明，unigram 情感模型 (kappa = 0.31) 优于 LIWC (kappa = 0.25)，最终 BERT 优于两种模型 (kappa = 0.48)。我们使用来自心理治疗的 97,497 个话语的数据库中的人类评分来训练 BERT 模型。我们的研究结果表明，unigram 情感模型 (kappa = 0.31) 优于 LIWC (kappa = 0.25)，最终 BERT 优于两种模型 (kappa = 0.48)。我们使用来自心理治疗的 97,497 个话语的数据库中的人类评分来训练 BERT 模型。我们的研究结果表明，unigram 情感模型 (kappa = 0.31) 优于 LIWC (kappa = 0.25)，最终 BERT 优于两种模型 (kappa = 0.48)。