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        <title>KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features</title>
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    <abstract>Sentiment analysis has always been an important research direction in natural language processing. The research can be divided into explicit sentiment analysis and implicit sentiment analysis according to whether there are sentiment words in language expression. There have been many research results in explicit sentiment analysis. However, implicit sentiment analysis is rarely studied. Compared with explicit sentiment expression, implicit sentiment expression usually omits a lot of knowledge and common sense, and context also has an important impact on implicit sentiment expression. In this paper, we use a knowledge graph to supplement implicit sentiment expression and propose a novel Implicit Sentiment Analysis model combining Knowledge enhancement and Context features (dubbed KC-ISA). The KC-ISA model can effectively integrate external knowledge and contextual features by the coattention mechanism. Finally, we conduct experiments on the SMP2019 implicit sentiment analysis dataset. Moreover, to verify the generality of the model, we also conduct experiments on two common sentiment analysis datasets. The results on three datasets show that our proposed KC-ISA model can achieve better results on text sentiment analysis.</abstract>
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        <url>https://aclanthology.org/2022.coling-1.601</url>
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%0 Conference Proceedings
%T KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features
%A Xu, Minghao
%A Wang, Daling
%A Feng, Shi
%A Yang, Zhenfei
%A Zhang, Yifei
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F xu-etal-2022-kc
%X Sentiment analysis has always been an important research direction in natural language processing. The research can be divided into explicit sentiment analysis and implicit sentiment analysis according to whether there are sentiment words in language expression. There have been many research results in explicit sentiment analysis. However, implicit sentiment analysis is rarely studied. Compared with explicit sentiment expression, implicit sentiment expression usually omits a lot of knowledge and common sense, and context also has an important impact on implicit sentiment expression. In this paper, we use a knowledge graph to supplement implicit sentiment expression and propose a novel Implicit Sentiment Analysis model combining Knowledge enhancement and Context features (dubbed KC-ISA). The KC-ISA model can effectively integrate external knowledge and contextual features by the coattention mechanism. Finally, we conduct experiments on the SMP2019 implicit sentiment analysis dataset. Moreover, to verify the generality of the model, we also conduct experiments on two common sentiment analysis datasets. The results on three datasets show that our proposed KC-ISA model can achieve better results on text sentiment analysis.
%U https://aclanthology.org/2022.coling-1.601
%P 6906-6915

Markdown (Informal)

[KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features](https://aclanthology.org/2022.coling-1.601) (Xu et al., COLING 2022)

• KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features (Xu et al., COLING 2022)

ACL

• Minghao Xu, Daling Wang, Shi Feng, Zhenfei Yang, and Yifei Zhang. 2022. KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6906–6915, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.