A System for Analyzing Topics and Evaluating Satisfaction Levels From Vietnamese Student Feedback Using Naive Bayes Classifier

As universities strive to improve teaching quality and enhance the overall learning experience, student feedback provides indispensable insights. The ability to automatically analyze open-ended comments has therefore become a crucial step toward developing efficient and evidence-based evaluation systems. This paper proposes a method based on the Naive Bayes Classifier (NBC) to address two tasks simultaneously: (i) classifying the topics of student feedback and (ii) evaluating the satisfaction levels expressed in the feedback. The dataset consists of comments collected during the Summer semester of the 2024–2025 academic year at Duy Tan University, which were preprocessed using techniques such as Term Frequency–Inverse Document Frequency (TF-IDF) and n-gram modeling. Experimental results demonstrate that the NBC model achieves an accuracy of 91.20% in topic classification and 90.62% in satisfaction classification. In addition, the paper provides visual illustrations through analytical charts for each class, highlighting the effectiveness of the model. The findings confirm that a simple yet powerful model such as NBC can be effectively applied in student feedback analysis, paving the way for the development of automatic evaluation systems in educational environments.