Natural language processing (NLP) algorithms have demonstrated significant capabilities in understanding responses to open-ended questions in survey data. However, the reliability and uncertainty of these methods on this task still need to be thoroughly investigated. To address this issue, this paper presents a comprehensive comparative analysis of various NLP methods for detecting fine-grained emotions in student responses about their mental health during the COVID-19 pandemic. The evaluated models include a Lexicon-based approach, the bag-of-words (BoW) model, Term Frequency-Inverse Document Frequency (TF-IDF), a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, MentalBERT, and OpenAI’s GPT-3.5. We carefully assess the efficacy of these models in accurately classifying emotions into predetermined categories using performance metrics such as accuracy and F1 score. Furthermore, model stability and distinguishing ability were quantified through repetitive cross-validation and the Area Under the Receiver Operating Characteristic Curve (AUC). The consistency of emotion detection across different models is also evaluated. The study highlights that the effectiveness of employing NLP methods for mental health analysis may vary depending on the emotions being analyzed, and their stability and uncertainty require thorough examination. Our work can provide valuable guidance for data scientists on applying NLP methods to survey data, particularly for understanding survey respondents’ emotions.
Maret et al. (Thu,) studied this question.