Social Research Institute, University of Utah, Salt Lake City, UT, USA.
Department of Educational Psychology, University of Utah, Salt Lake City, UT, USA.
Behav Res Methods. 2021 Oct;53(5):2069-2082. doi: 10.3758/s13428-020-01531-z. Epub 2021 Mar 22.
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，而分享情绪材料是心理治疗过程的核心。然而，系统地研究在心理治疗过程中发生的情绪交流模式的研究通常规模有限。传统的识别心理治疗中情绪的方法依赖于劳动密集型的观察者评分、治疗前或治疗后的客户或治疗师评分，或者涉及使用不考虑句子上下文的积极和消极词汇词典手动提取情绪评分。然而，机器学习算法领域的技术的最新进展，特别是自然语言处理，使得心理健康研究人员能够大规模识别治疗师-客户互动中的情绪，这是传统方法无法实现的。作为对 Tanana 等人（2016 年）先前发现的扩展尝试，我们将他们之前的情感模型与常见的基于词典的心理治疗模型 LIWC 和新的 NLP 模型 BERT 进行了比较。我们使用来自心理治疗数据库的 97497 条话语的人类评分来训练 BERT 模型。我们的研究结果表明，单词语义模型（kappa = 0.31）优于 LIWC（kappa = 0.25），最终 BERT 优于这两个模型（kappa = 0.48）。
