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Multi-view Opinion Mining with Deep Learning

  • Ping Huang
  • Xijiong Xie
  • Shiliang Sun
Article

Abstract

With the explosive growth of social media on the Internet, people are expressing an increasing number of opinions. As for objectives like business decision making and public opinion analysis, how to make the best of these precious opinionated words is a new challenge in the field of NLP. The field of opinion mining, or sentiment analysis, has become active in recent years. Since different kinds of deep neural networks differ in their structures, they are probably extracting different features. We investigated whether features generated by heterogeneous deep neural networks can be combined by multi-view learning to improve the overall performance. With document level opinion mining being the objective, we implemented multi-view learning based on heterogeneous deep neural networks. Experiments show that multi-view learning utilizing these heterogeneous features outperforms single-view deep neural networks. Our framework makes better use of single-view data.

Keywords

Multi-view learning Opinion mining Deep learning Heterogeneous neural networks 

Notes

Acknowledgements

The first two authors Ping Huang and Xijiong Xie are joint first authors. This work is sponsored by Shanghai Sailing Program. The corresponding author Shiliang Sun would also like to thank supports by NSFC Projects 61673179 and 61370175, and Shanghai Knowledge Service Platform Project (No. ZF1213). The work of Xijiong Xie was supported by the NSFC of Zhejiang Province under Project LQ18F020001.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiPeople’s Republic of China
  2. 2.The School of Information Science and EngineeringNingbo UniversityZhejiangPeople’s Republic of China

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