A System for Analyzing Topics and Evaluating Satisfaction Levels From Vietnamese Student Feedback Using Naive Bayes Classifier
Year: 2025
Type: article
Abstract: 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.
Authors: Hieu Ngo Van, Long Phan, Vinh Tran Nhat, Nin Ho Le Viet
Institution: Duy Tan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4415211410
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