<![CDATA[Comments in questionnaire feedback carry sentiment meanings, such as positive, negative, or neutral. Each review comment on training services requires prompt and accurate follow-up to improve service quality. However, sentiment classification often demands significant time and effort to determine sentiments accurately. This study aims to enhance efficiency and accuracy in sentiment classification for training questionnaires. A comparative analysis was conducted using three algorithms: Naïve Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The results indicate that SVM is the fastest and most accurate algorithm, with a training time of 3.067 seconds, 100 milliseconds faster than Naïve Bayes and 45.8 seconds faster than KNN. SVM achieved an accuracy of 60.81%, with an average sensitivity of 61%, specificity of 80%, and precision of 63%. Subsequently, this study integrated the Optimized Weight Evolutionary method to enhance SVM's accuracy and address attribute selection. Testing results showed a 2.16% improvement in SVM accuracy, bringing it to 63.10%. The training process was conducted on a dataset of 1,153 comments, with 90% of the data used for algorithm training. The combination of SVM and Optimized Weight Evolutionary proved effective in achieving more accurate sentiment classification. This study provides new insights into the application of sentiment classification, particularly for training feedback. Optimizing the algorithm can help training companies respond more effectively to comments and improve overall service quality.]]>
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