Sentiment Analysis and Topic Modelling of Students' Opinions of the Post-Pandemic Face-to-Face Classes
DOI:
https://doi.org/10.64169/dje.142Keywords:
Natural language processing, post-pandemic, sentiment analysis, topic modellingAbstract
After years of remote and blended learning, the Philippines' Basic Education system has fully resumed in-campus instruction, and this study examines students' views, opinions, and feelings toward the continuation of full face-to-face classes using sentiment analysis and topic modelling. An online survey was conducted via Google Forms with junior and senior high school students from public schools in Misamis Oriental, Philippines. Findings revealed that 72% of responses reflected positive emotions, 17% indicated negative emotions, and 11% were neutral, with most students expressing enthusiasm for social interaction, improved learning experiences, and a return to normalcy, while also noting concerns such as stress, financial strain, and classroom challenges. Word frequency and topic modelling further showed that the resumption of in-person classes has provided meaningful benefits while also exposing persistent issues that must be addressed. These results emphasise the importance of sustaining resilient, student-centred learning environments that go beyond merely returning to pre-pandemic routines, offering valuable insights for the Department of Education in developing strategies and policies that ensure accessibility, inclusivity, and long-term educational sustainability. Future research is recommended at the national level for broader generalisation.
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Copyright (c) 2025 Gernel Lumacad, Candaze Faye Tabamo, Alina Marie Tacandong, Ninio Crisjan Orboda, Neil Benedict Miranda, Mark Dave Saraus
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