Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations

Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opinion words, such as "good'' and "bad''. However, implicit sentiment data widely exists in the ABSA dataset, whose sentiment polarity is hard to determine due to the lack of distinct opinion words. To deal with implicit sentiment, this paper proposes an ABSA method that integrates explicit sentiment augmentations (ABSA-ESA) to add more sentiment clues. We propose an ABSA-specific explicit sentiment generation method to create such augmentations. Specifically, we post-train T5 by rule-based data and employ three strategies to constrain the sentiment polarity and aspect term of the generated augmentations. We employ Syntax Distance Weighting and Unlikelihood Contrastive Regularization in the training procedure to guide the model to generate the explicit opinion words with the same polarity as the input sentence. Meanwhile, we utilize the Constrained Beam Search to ensure the augmentations are aspect-related. We test ABSA-ESA on two ABSA benchmarks. The results show that ABSA-ESA outperforms the SOTA baselines on implicit and explicit sentiment accuracy.

Authors

• Jihong Ouyang College of Computer Science and Technology, Jilin University, China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE, Jilin University, China
• Zhiyao Yang College of Computer Science and Technology, Jilin University, China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China
• Silong Liang College of Computer Science and Technology, Jilin University, China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE, Jilin University, China
• Bing Wang Alibaba Group
• Yimeng Wang College of Computer Science and Technology, Jilin University, China
• Ximing Li Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University

Keywords

• NLP: Sentiment Analysis, Stylistic Analysis, and Argument Mining