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

Wipawan Buathong and Pita Jarupunphol

International Journal of Innovation and Learning, 2025, vol. 38, issue 1, 22-49

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.

Keywords: sentiment analysis; machine learning; educational services; natural language processing; data visualisation. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=147982 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijilea:v:38:y:2025:i:1:p:22-49

Access Statistics for this article

More articles in International Journal of Innovation and Learning from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().