Leveraging ChatGPT-Based Augmentation and Contrastive Learning for Chinese Massive Open Online Course Sentiment Analysis

This study addresses the unique challenges of sentiment analysis in Chinese massive open online course (MOOC) reviews, where pedagogically embedded language, intra-sentence sentiment shifts, and class imbalance complicate classification tasks. To tackle these domain-specific issues, we integrated ChatGPT-based data augmentation with contrastive learning within a Bidirectional Encoder Representations from Transformers (BERT)–Chinese framework. We evaluated ChatGPT-based augmentation (GPTaug), similar word replacement, and random word deletion under a dual-loss setup that combines supervised cross-entropy and InfoNCE (information noise-constrastive estimation) contrastive learning, focusing on how they enhance model performance across sentiment categories. The results revealed that the integration of contrastive learning with data augmentation strategies substantially improved sentiment classification in Chinese MOOC reviews. Especially, GPTaug demonstrated robust and balanced performance across polarity categories, particularly enhancing the detection of underrepresented neutral sentiments. These findings suggest that generative augmentation, when aligned with contrastive objectives, mitigates data sparsity and semantic ambiguity in educational sentiment analysis.