Work

Leveraging Large Language Models for Sentiment Analysis in Educational Contexts

Year: 2025

Type: book-chapter

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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250043

Authors: Arfan Ahmed, Sarah Aziz, Alaa Abd-Alrazaq, Rawan Alsaad, Javaid Sheikh

Institutions: Weill Cornell Medical College in Qatar, Weill Cornell Medicine

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

OpenAlex ID: W4409265009

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