Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis

Heyan Chai, Ziyi Yao, Siyu Tang, Ye Wang, Liqiang Nie, Binxing Fang, Qing Liao

Abstract
Aspect-based sentiment analysis (ABSA) aims to align aspects and corresponding sentiment expressions, so as to identify the sentiment polarities of specific aspects. Most existing ABSA methods focus on mining syntactic or semantic information, which still suffers from noisy interference introduced by the attention mechanism and dependency tree when multiple aspects exist in a sentence. To address these issues, in this paper, we revisit ABSA from a novel perspective by proposing a novel scope-assisted multi-view graph contrastive learning framework. It not only mitigates noisy interference for better locating aspect and its corresponding sentiment opinion with aspect-specific scope, but also captures the correlation and difference between sentiment polarities and syntactic/semantic information. Extensive experiments on five benchmark datasets show that our proposed approach substantially outperforms state-of-the-art methods and verifies the effectiveness and robustness of our model.
Anthology ID:
2023.findings-emnlp.727
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10902–10913
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.727
DOI:
10.18653/v1/2023.findings-emnlp.727
Bibkey:
Cite (ACL):
Heyan Chai, Ziyi Yao, Siyu Tang, Ye Wang, Liqiang Nie, Binxing Fang, and Qing Liao. 2023. Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10902–10913, Singapore. Association for Computational Linguistics.
Cite (Informal):
Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis (Chai et al., Findings 2023)
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