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This study explores innovative approaches for predicting Vietnamese students’ sentiments regarding university service quality by applying machine learning models through Apache PySpark. Data collected through online surveys is analyzed using multiple models, including Logistic Regression, Decision Trees, Random Forest, and Naive Bayes. Among these, Logistic Regression achieved the highest accuracy at 86.7%, followed by Naive Bayes at 75.5%. These results demonstrate the potential of PySpark for scalable and efficient sentiment analysis in the educational domain.

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A Novel PySpark Model in Predicting Vietnamese Students’ Sentiment

  • 会议论文

  • 发表日期：

    2025年

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  • Nguyen Minh Tuan,
  • Phayung Meesad,
  • Duong Van Hieu

摘要

This study explores innovative approaches for predicting Vietnamese students’ sentiments regarding university service quality by applying machine learning models through Apache PySpark. Data collected through online surveys is analyzed using multiple models, including Logistic Regression, Decision Trees, Random Forest, and Naive Bayes. Among these, Logistic Regression achieved the highest accuracy at 86.7%, followed by Naive Bayes at 75.5%. These results demonstrate the potential of PySpark for scalable and efficient sentiment analysis in the educational domain.