  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.
