Research Article
Sentiment Analysis of Learner Feedback in Distance Education in Vietnam Using Machine Learning Models
Issue:
Volume 15, Issue 6, December 2026
Pages:
256-270
Received:
9 October 2026
Accepted:
3 November 2026
Published:
10 November 2026
DOI:
10.11648/j.edu.20261506.11
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Abstract: In the context of the rapidly accelerating digital transformation of education, distance learning has become an indispensable part of higher education in Vietnam. To improve teaching quality, educational institutions typically rely on periodic surveys to collect student opinions; however, this approach reflects only the views of a small group at a particular point in time and lags behind reality, whereas discussions and reviews on social media platforms such as Facebook, Threads and TikTok are updated continuously and express learners' opinions objectively. To leverage this resource, the research team collected and constructed the V-LearnerSentiment dataset comprising 6,366 valid observations, filtered and manually annotated from these platforms, to support sentiment analysis and aspect-based sentiment classification. The study compares the performance of traditional machine learning models (Support Vector Machine - SVM, Decision Tree - DT, Random Forest - RF), a deep learning model (Transformer) and large language models (LLMs) based on Accuracy, Precision, Recall and F1-score, using macro and weighted averages to reflect sentiment classification effectiveness under imbalanced data conditions. Experimental results show that Gemini 2.5 achieves the highest Accuracy (86.22%), demonstrating its ability to effectively handle diverse and context-dependent feedback, while the specialized fine-tuned Transformer PhoBERT achieves an Accuracy of 82.6% and better identifies negative feedback belonging to the minority class. In addition to overall sentiment analysis, the team implemented aspect-based sentiment analysis (ABSA) through a two-step procedure: the LDA model was used as a data exploration tool to extract and propose an Aspect Schema consisting of 5 main groups (Tuition & Costs, Degree & Value, Program & Quality, Schedule & Learning Mode, Counseling & Procedures), and a large language model was then employed to extract the context and classify the corresponding sentiment polarity for each aspect, transforming unstructured text data into measurable quantitative indicators. This approach not only enhances the interpretability of the results but also clearly identifies learners' main concerns, thereby providing a practical basis for improving the quality of distance education in Vietnam.
Abstract: In the context of the rapidly accelerating digital transformation of education, distance learning has become an indispensable part of higher education in Vietnam. To improve teaching quality, educational institutions typically rely on periodic surveys to collect student opinions; however, this approach reflects only the views of a small group at a ...
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,
Ngo Phuong Linh
