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        <title>A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification</title>
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    <abstract>Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge. In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation words, intensity words) into the deep neural network via attention mechanisms. By using various types of sentiment resources, MEAN utilizes sentiment-relevant information from different representation sub-spaces, which makes it more effective to capture the overall semantics of the sentiment, negation and intensity words for sentiment prediction. The experimental results demonstrate that MEAN has robust superiority over strong competitors.</abstract>
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%0 Conference Proceedings
%T A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification
%A Lei, Zeyang
%A Yang, Yujiu
%A Yang, Min
%A Liu, Yi
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F lei-etal-2018-multi
%X Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge. In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation words, intensity words) into the deep neural network via attention mechanisms. By using various types of sentiment resources, MEAN utilizes sentiment-relevant information from different representation sub-spaces, which makes it more effective to capture the overall semantics of the sentiment, negation and intensity words for sentiment prediction. The experimental results demonstrate that MEAN has robust superiority over strong competitors.
%R 10.18653/v1/P18-2120
%U https://aclanthology.org/P18-2120
%U https://doi.org/10.18653/v1/P18-2120
%P 758-763

Markdown (Informal)

[A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification](https://aclanthology.org/P18-2120) (Lei et al., ACL 2018)

• A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (Lei et al., ACL 2018)

ACL

• Zeyang Lei, Yujiu Yang, Min Yang, and Yi Liu. 2018. A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 758–763, Melbourne, Australia. Association for Computational Linguistics.