Topic Modelling and Sentimental Analysis of Students’ Reviews

Abstract

Globally, educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic. The fundamental concern has been the continuance of education. As a result, several novel solutions have been developed to address technical and pedagogical issues. However, these were not the only difficulties that students faced. The implemented solutions involved the operation of the educational process with less regard for students' changing circumstances, which obliged them to study from home. Students should be asked to provide a full list of their concerns. As a result, student reflections, including those from Saudi Arabia, have been analysed to identify obstacles encountered during the COVID-19 pandemic. However, most of the analyses relied on closed-ended questions, which limited student involvement. To delve into students' responses, this study used open-ended questions, a qualitative method (content analysis), a quantitative method (topic modelling), and a sentimental analysis. This study also looked at students' emotional states during and after the COVID-19 pandemic. In terms of determining trends in students' input, the results showed that quantitative and qualitative methods produced similar outcomes. Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study. Furthermore, topic modelling has revealed that the majority of difficulties are more related to the environment (home) and social life. Students were less accepting of online learning. As a result, it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot, such as social interaction and effective eye-to-eye communication.

Revolutionizing Education: Navigating the New Landscape Post-COVID-19: A Scoping Review

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Relationship between mental health and job competence of college teachers: Application of sentiment analysis algorithm

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