Abstract
Student evaluation of teaching (SET) is an ad-hoc way of assessing teaching effectiveness in higher education institutions. In this paper, we present an approach to analyzing sentiments expressed in SET comments using a large language model (LLM). By employing natural language processing techniques, we extract and analyze sentiments expressed by students when the course has ended, aiming to provide educators and administrators with valuable insights into teaching quality and elements to improve teaching practice. Our study demonstrates the effectiveness of LLMs in sentiment analysis of comments, highlighting their potential to enhance the evaluation process. Our experiments with a crowdsourced tagged dataset show a 93% of accuracy in the classification of feedback messages. We discuss the implications of our findings for educational institutions and propose future directions for research in this domain.
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