A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification

Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu


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.
Anthology ID:
P18-2120
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
758–763
URL:
https://www.aclweb.org/anthology/P18-2120
DOI:
10.18653/v1/P18-2120
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PDF:
https://www.aclweb.org/anthology/P18-2120.pdf
Video:
 https://vimeo.com/285806141
Presentation:
 P18-2120.Presentation.pdf