A data augmentation technique based on text for Vietnamese sentiment analysis

Online opinions are used as a data source that contains relevant information about customer sentiments toward a product or service. This can be used to make a specific decision for customers and management. In order to achieve the good models for sentiment analysis, we require a large human-labeled data which is costly to obtain. This paper proposes an approach based on text data augmentation based on product reviews in Vietnamese language. Several basic techniques are applied to generate more comments by random insertions, substitutions. The experimental results demonstrate the efficiency of the proposed approach.