Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis
Jin Cui, Fumiyo Fukumoto, Xinfeng Wang, Yoshimi Suzuki, Jiyi Li, Wanzeng Kong
Abstract
Aspect-based sentiment analysis (ABSA) has been widely studied since the explosive growth of social networking services. However, the recognition of implicit sentiments that do not contain obvious opinion words remains less explored. In this paper, we propose aspect-category enhanced learning with a neural coherence model (ELCoM). It captures document-level coherence by using contrastive learning, and sentence-level by a hypergraph to mine opinions from explicit sentences to aid implicit sentiment classification. To address the issue of sentences with different sentiment polarities in the same category, we perform cross-category enhancement to offset the impact of anomalous nodes in the hypergraph and obtain sentence representations with enhanced aspect-category. Extensive experiments on benchmark datasets show that the ELCoM achieves state-of-the-art performance. Our source codes and data are released at https://github.com/cuijin-23/ELCoM.- Anthology ID:
- 2023.findings-emnlp.759
- 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:
- 11345–11358
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.759
- DOI:
- 10.18653/v1/2023.findings-emnlp.759
- Cite (ACL):
- Jin Cui, Fumiyo Fukumoto, Xinfeng Wang, Yoshimi Suzuki, Jiyi Li, and Wanzeng Kong. 2023. Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11345–11358, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis (Cui et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.759.pdf