Sentiment Classification of Higher Education Reviews to Analyze Students’ Engagement and Psychology Interventions Using Deep Learning Techniques

Abstract

AbstractGlobally, higher education institutions are closed due to the COVID-19 pandemic. The sudden shift to online education excites most teachers and students. The professors are researching online learning platforms. They are only involved in face-to-face teaching in traditional teaching platforms. There are many concerns about the quality of online education. This paper proposes a framework for comparing online learning with traditional learning using emotions, learner perception, instructors, student engagement, understanding, effectiveness, learning outcome, peer collaboration, constraints, and comparisons. Deep learning algorithms like LSTM, GRU, and RNN classify the reviews. Students are positive during online learning in higher education according to LSTM, GRU, and RNN experimental analysis. Students are becoming more comfortable with online learning environments for higher education, according to detailed survey results.KeywordsStudent engagementPsychology interventionsSentimentsReviewsOnline learningHigher education