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Assessing the emotional feedback of teaching and learning service beneficiaries using machine learning on text comments

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  • Wipawan Buathong
  • Pita Jarupunphol

Abstract

This study evaluates emotional responses to educational services using advanced machine-learning techniques to categorise sentiment in feedback. The dataset includes 1,033 comments from 402 individuals, collected via various platforms. Three algorithms were applied: random forest, Naïve Bayes, and long short-term memory (LSTM). The ten-folds cross-validation method ensured model robustness. Random forest achieved the highest F1-score of 0.833, LSTM at 0.827, and Naïve Bayes at 0.807. The analysis indicated that neutral sentiments were most accurately predicted, followed by positive and negative sentiments. Additionally, latent Dirichlet allocation (LDA) identified key themes within the feedback. Positive topics included teaching effectiveness, subject variety, and professional development. Negative topics highlighted issues with technology and resources. Word cloud dashboards focused on curriculum design, learning support mechanisms, and instructional quality. These insights are crucial for enhancing the effectiveness of teaching services, indicating areas of strength and potential improvement.

Suggested Citation

  • Wipawan Buathong & Pita Jarupunphol, 2025. "Assessing the emotional feedback of teaching and learning service beneficiaries using machine learning on text comments," International Journal of Innovation and Learning, Inderscience Enterprises Ltd, vol. 38(1), pages 22-49.
    • Handle: RePEc:ids:ijilea:v:38:y:2025:i:1:p:22-49
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