Automated Utterance Labeling of Conversations Using Natural Language Processing
This work addresses the need for efficient analysis of conversational data in psychology, though it appears incremental as it builds on existing NLP methods for a specific domain.
The study tackled the problem of automating utterance labeling in psychological conversational data, which faces challenges like multilabel classification and limited data, by proposing a deep learning method with domain adaptation (RoBERTa-CON) and a hierarchical labeling system, resulting in it outperforming other machine learning methods.
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.