Abstract
The mental health of students plays an important role in their overall wellbeing and academic performance. Growing pressure from academics, co-curricular activities such as sports and personal challenges highlight the need for modern methods of monitoring mental health. Traditional approaches, such as self-reported surveys and psychological evaluations, can be time-consuming and subject to bias. With advancement in artificial intelligence (AI), particularly in natural language processing (NLP), sentiment analysis has emerged as an effective technique for identifying mental health patterns in textual data. However, analyzing students' mental health remains a challenging task due to the intensity of emotional expressions, linguistic variations, and context-dependent sentiments. In this study, our primary objective was to investigate the mental health of students by conducting sentiment analysis using advanced deep learning models. To accomplish this task, state-of-the-art Large Language Model (LLM) approaches, such as RoBERTa (a robustly optimized BERT approach), RoBERTa-Large, and ELECTRA, were used for empirical analysis. RoBERTa-Large, an expanded architecture derived from Google's BERT, captures complex patterns and performs more effectively on various NLP tasks. Among the applied algorithms, RoBERTa-Large achieved the highest accuracy of 97%, while ELECTRA yielded 91% accuracy on a multi-classification task with seven diverse mental health status labels. These results demonstrate the potential of LLM-based approaches for predicting students' mental health, particularly in relation to the effects of academic and physical activities.