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        <title>Aspect Sentiment Classification with Aspect-Specific Opinion Spans</title>
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        <namePart type="given">Lu</namePart>
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        <namePart type="given">Lidong</namePart>
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    <name type="personal">
        <namePart type="given">Fei</namePart>
        <namePart type="family">Huang</namePart>
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    <abstract>Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our analysis demonstrates that our model can capture aspect-specific opinion spans.</abstract>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Aspect Sentiment Classification with Aspect-Specific Opinion Spans
%A Xu, Lu
%A Bing, Lidong
%A Lu, Wei
%A Huang, Fei
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F xu-etal-2020-aspect
%X Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our analysis demonstrates that our model can capture aspect-specific opinion spans.
%R 10.18653/v1/2020.emnlp-main.288
%U https://aclanthology.org/2020.emnlp-main.288
%U https://doi.org/10.18653/v1/2020.emnlp-main.288
%P 3561-3567

Markdown (Informal)

[Aspect Sentiment Classification with Aspect-Specific Opinion Spans](https://aclanthology.org/2020.emnlp-main.288) (Xu et al., EMNLP 2020)

• Aspect Sentiment Classification with Aspect-Specific Opinion Spans (Xu et al., EMNLP 2020)

ACL

• Lu Xu, Lidong Bing, Wei Lu, and Fei Huang. 2020. Aspect Sentiment Classification with Aspect-Specific Opinion Spans. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3561–3567, Online. Association for Computational Linguistics.