Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations

• DOI: 10.48550/arxiv.2312.10961
• Publication Date: 2023-01-01
• Authors: Jihong Ouyang; Zhiyao Yang; Silong Liang; Bing Wang; Yimeng Wang; Ximing Li
• Keywords: Sentiment Analysis; Regularization

Aspect-based sentiment analysis (ABSA), a fine-grained classification task, has received much attention recently. Many works investigate information through opinion words, such as ''good'' and ''bad''. However, implicit widely exists in the ABSA dataset, which refers to sentence containing no distinct words but still expresses aspect term. To deal with sentiment, this paper proposes an method that integrates explicit augmentations. And we propose ABSA-specific augmentation create Specifically, post-trains T5 by rule-based data. We employ Syntax Distance Weighting Unlikelihood Contrastive Regularization training procedure guide model generate sentiment. Meanwhile, utilize Constrained Beam Search ensure contains terms. test ABSA-ESA on two of most popular benchmarks ABSA. The results show outperforms SOTA baselines accuracy.