Skip to main content
SpringerLink
Log in

Multi-view Opinion Mining with Deep Learning

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. https://code.google.com/p/word2vec/.

References

  1. Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167

    Article  Google Scholar 

  2. Sun S, Luo C, Chen J (2017) A review of natural language processing techniques for opinion mining systems. Inf Fusion 36:10–25

    Article  Google Scholar 

  3. Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. ArXiv preprint arXiv:1404.2188

  4. Irsoy O, Cardie C (2014) Opinion mining with deep recurrent neural networks. In Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 720–728

  5. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  6. Yu J, Tao D, Wang M, Rui Y (2015) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45:767–779

    Article  Google Scholar 

  7. Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23:2019–2032

    Article  MathSciNet  Google Scholar 

  8. Yu J, Yang X, Gao F, Tao D (2016) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47:4014–4024

    Article  Google Scholar 

  9. Tao D, Hong C, Yu J, Wan J, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24:5659–5670

    Article  MathSciNet  Google Scholar 

  10. Hong C, Yu J, Tao D, Wang M (2015) Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron 62:3742–3751

    Article  Google Scholar 

  11. Zhao J, Xie X, Xu X, Sun S (2017) Multi-view learning overview: recent progress and new challenges. Inf Fusion 38:43–54

    Article  Google Scholar 

  12. Sun S, Chao G (2013) Multi-view maximum entropy discrimination. In: Proceedings of the 23rd international joint conference on artificial intelligence, pp 1706–1712

  13. Yu J, Rui Y, Chen B (2013) Exploiting click constraints and multi-view features for image re-ranking. IEEE Trans Multimed 16:159–168

    Article  Google Scholar 

  14. Yu J, Wang M, Tao D (2012) Semi-supervised multiview distance metric learning for cartoon synthesis. IEEE Trans Multimed 21:4636–4648

    MATH  Google Scholar 

  15. Hardoon DR, Szedmak SR, Shawe-Taylor JR (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 12:2639–2664

    Article  Google Scholar 

  16. Niu T, Zhu S, Pang L, El Saddik A (2016) Sentiment analysis on multi-view social data. In: Proceedings of the international conference on multimedia modeling, pp 15–27

  17. Tang J, Hu X, Gao H, Liu H (2013) Unsupervised feature selection for multi-view data in social media. In: Proceedings of the SIAM international conference on data mining. SIAM, pp 270–278

  18. Wan X (2009) Co-training for cross-lingual sentiment classification. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP, vol 1. Association for Computational Linguistics, pp 235–243

  19. Hajmohammadi MS, Ibrahim R, Selamat A (2014) Bi-view semi-supervised active learning for cross-lingual sentiment classification. Inf Process Manag 50(5):718–732

    Article  Google Scholar 

  20. Le Cun Y, Jackel L, Boser B, Denker J, Graf H, Guyon I, Henderson D, Howard R, Hubbard W (1989) Handwritten digit recognition: applications of neural network chips and automatic learning. IEEE Commun Mag 27:41–46

    Article  Google Scholar 

  21. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  22. Tian DP (2013) A review on image feature extraction and representation techniques. Int J Multimed Ubiquitous Eng 8(4):385–396

    Google Scholar 

  23. Egmont-Petersen M, de Ridder D, Handels H (2002) Image processing with neural networksa review. Pattern Recognit 35(10):2279–2301

    Article  Google Scholar 

  24. Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681

    Article  Google Scholar 

  25. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. ArXiv preprint arXiv:1506.00019

  26. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  27. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. ArXiv preprint arXiv:1406.1078

  28. Turian J, Ratinov L, Bengio Y (2010) Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 384–394

  29. Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol 1. Association for Computational Linguistics, pp 142–150

  30. Kim Y (2014) Convolutional neural networks for sentence classification. ArXiv preprint arXiv:1408.5882

  31. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. ArXiv preprint arXiv:1409.0473

  32. Farquhar J, Hardoon D, Meng H, Shawe-taylor JS, Szedmak S (2006) Two view learning: SVM-2K, theory and practice. Adv Neural Inf Process Syst 18:355–362

    Google Scholar 

  33. Chao G, Sun S (2016) Alternative multiview maximum entropy discrimination. IEEE Trans Neural Netw Learn Syst 27:1445–1456

    Article  MathSciNet  Google Scholar 

  34. Mao L, Sun S (2016) Soft margin consistency based scalable multi-view maximum entropy discrimination. In: Proceedings of the 25th international joint conference on artificial intelligence, pp 1839–1845

Download references

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.

Author information

Author notes

  1. Ping Huang and Xijiong Xie contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Technology, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, People’s Republic of China

    Ping Huang & Shiliang Sun

  2. The School of Information Science and Engineering, Ningbo University, Zhejiang, 315211, People’s Republic of China

    Xijiong Xie

Authors
  1. Ping Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xijiong Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shiliang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shiliang Sun.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, P., Xie, X. & Sun, S. Multi-view Opinion Mining with Deep Learning. Neural Process Lett 50, 1451–1463 (2019). https://doi.org/10.1007/s11063-018-9935-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9935-0

Keywords

Search

Navigation