A Data Augmentation Approach to Sentiment Analysis of MOOC Reviews

To address the lack of Chinese online course review corpora for aspect-based sentiment analysis, we propose Semantic Token Augmentation and Replacement (STAR), a semantic-relative distance-based data augmentation method. STAR leverages natural language processing techniques such as word embedding and semantic similarity to extract high-frequency words near aspect terms, learns their word vectors to obtain synonyms and replaces these words to enhance sentence diversity while maintaining semantic consistency. Experiments on a Chinese MOOC dataset show STAR improves Macro-F1 scores by 3.39%-8.18% for LCFS-BERT and 1.66%-8.37% for LCF-BERT compared to baselines. These results demonstrate STAR's effectiveness in improving the generalization ability of deep learning models for Chinese MOOC sentiment analysis.