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An improved context-aware sentiment analysis of student comments on social networks based on ChatGPT

Qaffas Alaa Asim

Computer Science

2025

T. 26 (1)

101--131

The widespread use of social networks has provided a variety of active, dynamic, and popular platforms for students to express their opinions and sentiments.These data are increasingly being exploited and integrated into university information systems to better govern and manage universities and improve educational quality. The analysis of such data can offer valuable insights into student experiences and attitudes towards various educational aspects including courses, professors, events, and facilities. However, automatic opinion mining in this context is challenging due to the difficulty of analyzing some languages suchas Arabic, the variety of used languages, the presence of informal language, theuse of emoticons and emoji, sarcasm, and the need to consider the surrounding context. To deal with all these challenges, we propose a novel approach for an effective sentiment analysis of student comments on the X platform (Twitter).The proposed approach allows the collection of student comments from public Twitter pages and automatically classifies comments into positive, negative, and neutral. The new approach is based on ChatGPT capabilities, supports three languages: English, Arabic, and colloquial Arabic, and integrates a news coring method that measures both the positiveness and subjectivity of student comments. Experiments performed on simulated and real public Twitter pages of five Saudi high education institutions showed the performance of the proposed tool to analyze and summarize collected data automatically.