Conversational data is essential in psychology because it can help researchers understand individuals cognitive processes, emotions, and behaviors. Utterance labelling is a common strategy for analyzing this type of data. The development of NLP algorithms allows researchers to automate this task. However, psychological conversational data present some challenges to NLP researchers, including multilabel classification, a large number of classes, and limited available data. This study explored how automated labels generated by NLP methods are comparable to human labels in the context of conversations on adulthood transition. We proposed strategies to handle three common challenges raised in psychological studies. Our findings showed that the deep learning method with domain adaptation (RoBERTa-CON) outperformed all other machine learning methods; and the hierarchical labelling system that we proposed was shown to help researchers strategically analyze conversational data. Our Python code and NLP model are available at https://github.com/mlaricheva/automated_labeling.

本文研究了机器学习在心理会话数据的自动标注上的可行性，提出了解决多标签分类、大量类别、数据限制等问题的方法，并通过实验表明RoBERTa-CON算法在性成年人转变话题上的表现优于其他方法，同时本文还提出了一种层次化标注系统，可帮助研究者分析会话数据。

使用自然语言处理自动标注对话的话语