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 pro-pose 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.

@article{Li2024,
  title     = {A Data Augmentation Approach to Sentiment Analysis of MOOC Reviews},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Guangmin Li and Long Zhou and Qiang Tong and Yi Ding and Xiaolin Qi and Hang Liu},
  doi       = {10.14569/IJACSA.2024.01508122},
  url       = {https://doi.org/10.14569/IJACSA.2024.01508122}
}