@inproceedings{cui-etal-2023-aspect,
title = "Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis",
author = "Cui, Jin and
Fukumoto, Fumiyo and
Wang, Xinfeng and
Suzuki, Yoshimi and
Li, Jiyi and
Kong, Wanzeng",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.759",
doi = "10.18653/v1/2023.findings-emnlp.759",
pages = "11345--11358",
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 \url{https://github.com/cuijin-23/ELCoM}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis
%A Cui, Jin
%A Fukumoto, Fumiyo
%A Wang, Xinfeng
%A Suzuki, Yoshimi
%A Li, Jiyi
%A Kong, Wanzeng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cui-etal-2023-aspect
%X 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.
%R 10.18653/v1/2023.findings-emnlp.759
%U https://aclanthology.org/2023.findings-emnlp.759
%U https://doi.org/10.18653/v1/2023.findings-emnlp.759
%P 11345-11358
Markdown (Informal)
[Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis](https://aclanthology.org/2023.findings-emnlp.759) (Cui et al., Findings 2023)
ACL