<![CDATA[Open-ended course evaluation comments provide detailed information about students' learning experiences, but large volumes of text are difficult to review consistently by hand. This study compares Decision Tree, Support Vector Machine (SVM), Random Forest, and Gradient Boosting for sentiment classification of Computer Engineering student comments. The initial dataset contained 1,075 comments. Compound-score labeling produced 1,065 neutral comments (99.07%), five negative comments (0.47%), and five positive comments (0.47%). After incomplete records were removed, 415 observations remained for modeling, and the test set contained 207 observations. Before SMOTE, Random Forest and SVM achieved 98.06% accuracy, while Decision Tree and Gradient Boosting achieved 97.58%. After SMOTE, SVM provided the best balance, with 98.55% accuracy, 66.18% macro precision, 66.67% macro recall, and 66.42% macro F1-score. Although overall accuracy was high, the positive class was not detected. Therefore, SVM was the most balanced model in this experiment, while macro-averaged metrics and class-wise errors remain essential when evaluating highly imbalanced sentiment data]]>
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