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Neural Sentiment Classification with Social Feedback Signals

  • Tao Wang
  • Yuanxin Ouyang
  • Wenge Rong
  • Zhang Xiong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

Neural network methods have achieved promising results for document-level sentiment classification. Since the popularity of Web 2.0, a growing number of websites provide users with voting and feedback systems (or called social feedback system). However, most existing sentiment classification models only focus on text information while ignoring the social feedback signals from fellow users, despite the association between voting and review predicting. To address this issue, first, we conduct empirical analysis based on a large-scale review dataset to verify the relevance between the social feedback signals and the review predicting. Afterward, we build a hierarchical attention model to generate sentence-level and document-level representations. Finally, we feed the social feedback information into word level and sentence level attention layers. Extensive experiments demonstrate that our model can significantly outperform several strong baseline methods and social feedback signals can promote the performance of attention model.

Keywords

Sentiment classification Social feedback signal Attention mechanism Recurrent neural network 

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No. 61332018), and SKLSDE project under Grant No. SKLSDE-2017ZX.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringBeihang UniversityBeijingChina

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