Leveraging Large Language Models for Sentiment Analysis in Educational Contexts

Abstract

This short communication presents preliminary findings on the application of Large Language Models (LLMs) for sentiment analysis in educational settings. By analyzing qualitative descriptions derived from student reports, we aimed to assess students' emotional states and attitudes towards their academic performance. The sentiment analysis provided valuable insights into student engagement and areas requiring attention. Our results indicate that LLMs can effectively process and analyze textual data, offering a more nuanced understanding of student sentiments compared to traditional coding methods. This approach highlights the potential of LLMs in enhancing educational assessments and interventions.