Enhancing aspect-category sentiment analysis via syntactic data augmentation and knowledge enhancement

Abstract

The goal of aspect-category sentiment analysis (ACSA) is to predict the sentiment polarity toward a specific aspect category from reviewers’ expressed opinions in a sentence. With the boom of pretrained language models, various relative methods have achieved significant improvements in ACSA. However, two major issues still remain to be solved. First, most of these studies usually follow the canonical method of fine-tuning on limited labeled data, neglecting to leverage external knowledge to further enhance ACSA performance. Second, aspect categories are usually abstract concepts that are mentioned explicitly or implicitly, and the corresponding different sentiment polarities are not easy to accurately recognize. To address these issues, we first transform the ACSA task into a sentence-pair classification task with natural language inference, constructing synthetic sentences as hypotheses based on the predefined aspect categories and the prompt-generation sentence template. Then the model applies a passivization transformation to the synthetic sentences and generates more syntactic data to augment the limited training data. Furthermore, we enhance ACSA with curated knowledge from a common sense knowledge graph. Finally, different representations are synergistically fused with a gating mechanism to output richer sentiment features and enable context-, syntax-, and knowledge-aware predictions. Experimental results on three challenging benchmark datasets show that the proposed model outperforms some competitive baselines.