Fine-Tuned BERT Model for Sentiment Classification of Chinese MOOCs

This study explores the effectiveness of a fine-tuned BERT model for sentiment classification of Chinese MOOC reviews, focusing on the linguistic and cultural nuances of Chinese learners. The empirical evaluation shows that the fine-tuned BERT model significantly outperforms traditional ma-chine learning models, including random forest, support vector machines, long short-term memory, and convolutional neural network, achieving an Accuracy of 96.33% and an F1-score of 72.57%. The fine-tuned BERT model excels at identifying positive sentiment (an Accuracy of 0.99, a F1-score of 0.99) but struggles with negative sentiment classification, showing lower performance likely due to class imbalance and the nuanced nature of negative emotions. Despite these challenges, the fine-tuned BERT model’s ability to effectively classify positive and neutral sentiments indicates its potential for real-time sentiment monitoring in MOOCs, offering insights that can inform adaptive learning systems. This work contributes to the field of sentiment analysis in non-English MOOCs, particularly focusing on the context of Chinese learners, and demonstrates the significance of adopting culturally and linguistically adapted models to detect the subtleties of student feed-back.