Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models
It addresses the challenge of systematically assessing mental health services for college students, though it is incremental as it applies existing methods to a new domain-specific dataset.
This paper tackled the problem of evaluating mental health support in colleges by analyzing student sentiments using large language models (LLMs) on a new dataset, SMILE-College, finding that GPT-3.5 and BERT achieved the best performance.
Mental health support in colleges is vital in educating students by offering counseling services and organizing supportive events. However, evaluating its effectiveness faces challenges like data collection difficulties and lack of standardized metrics, limiting research scope. Student feedback is crucial for evaluation but often relies on qualitative analysis without systematic investigation using advanced machine learning methods. This paper uses public Student Voice Survey data to analyze student sentiments on mental health support with large language models (LLMs). We created a sentiment analysis dataset, SMILE-College, with human-machine collaboration. The investigation of both traditional machine learning methods and state-of-the-art LLMs showed the best performance of GPT-3.5 and BERT on this new dataset. The analysis highlights challenges in accurately predicting response sentiments and offers practical insights on how LLMs can enhance mental health-related research and improve college mental health services. This data-driven approach will facilitate efficient and informed mental health support evaluation, management, and decision-making.