Sentiment Analysis of Students Feedback in Online Courses Using Supervised, Ensemble, and Transfer Learning Methods

Abstract

Student feedback and reviews are crucial data that can offer valuable perspectives and insightful observations for enhancing the learning and teaching approach in the classroom. The process of instructors manually analyzing and reviewing numerous feedback to obtain valuable insights is laborious and time-consuming. Sentiment analysis, a subfield of natural language processing, offers the tools to examine, extract, and measure subjective information from textual data. Hence, employing predictive models for sentiment analysis can effectively supplant the laborious job and completely resolve the issue. This work focuses on the implementation of supervised learning methods, ensemble learning methods, and transfer learning techniques for sentiment analysis of student comments. The models employed in various methodologies strive to forecast the sentiment of feedback in an online course as either positive, negative, or neutral. An evaluation was carried out to compare all the machine learning approaches to identify the most precise model with strong performance. The evaluation results indicate that DistilBERT, when used in transfer learning, achieves the highest performance in predicting the sentiment of student feedback, with an F-measure score of 95.49%.