Machine Learning Based Sentiment Analysis of Student Feedback in Higher Education - Ramani Jaydeep Ramniklal (International Journal of Data Structure Studies, 2026)
Educational institutions routinely collect feedback from students to understand their perceptions of academic programs, infrastructure, and campus facilities, to improve the overall quality of the college environment. In current practice, feedback is often gathered using numerical or grade-based rating systems, which tend to oversimplify student opinions and may overlook important details related to their level of satisfaction. In contrast, open-ended textual feedback allows students to clearly express their views, concerns, and suggestions, offering valuable insights that can support meaningful institutional improvements. This study proposes an approach for analyzing student sentiment by applying sentiment analysis techniques to textual feedback obtained from students in Gujarat. Machine learning models, including multinomial Naïve Bayes (MNB), support vector machine (SVM), and random forest (RF), are employed to classify the sentiments expressed in the feedback data. A comparative performance analysis of these algorithms is conducted using evaluation metrics such as accuracy and F-score to determine their effectiveness. The experimental results demonstrate that, among the evaluated methods, the MNB classifier achieves superior performance in most cases. Its effectiveness is particularly notable in processing and classifying text-based data, making it a suitable choice for sentiment analysis tasks in educational feedback systems.
Keywords: Feedback; Machine Learning; algorithms; multinomial Naïve Bayes; sentiment analysis; support vector machine (SVM)