July 19, 2025

Towards Effective Distance Education Implementation: Utilizing Descriptive Analytics, Opinion Mining, and Sentiment Analysis for Online Education Mentors and Learning Materials

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Sentiment analysis improves feedback evaluation in online education, suggesting enhanced mentor performance and course delivery.

Automated sentiment analysis achieves an accuracy of 88.6%, indicating strong performance in feedback evaluation and insights.
The VADER sentiment tool was effective in labeling student feedback as positive, negative, or neutral, enhancing data classification.
Analysis employed a neural network model using LSTM, focusing on improving sentiment classification in distant learning environments.
Findings emphasize the importance of balanced training datasets for accurate classification of various sentiment types.

Soriano et al. (2025) studied this question.

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