Skip to main content
SpringerLink
Log in

Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding

  • Conference paper
  • First Online:
Cloud Computing and Security (ICCCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10040))

Included in the following conference series:

Abstract

Long Short-Term Memory network have been successfully applied to sequence modeling task and obtained great achievements. However, Chinese text contains richer syntactic and semantic information and has strong intrinsic dependency between words and phrases. In this paper, we propose Bidirectional Long Short-Term Memory (BLSTM) with word embedding for Chinese sentiment analysis. BLSTM can learn past and future information and capture stronger dependency relationship. Word embedding mainly extract words’ feature from raw characters input and carry important syntactic and semantic information. Experimental results show that our model achieves 91.46 % accuracy for sentiment analysis task.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, pp. 627–666 (2010)

    Google Scholar 

  2. Balahur, A., Steinberger, R., Kabadjov, M.: Sentiment analysis in the news. Infrared Phys. Technol. 65, 94–102 (2014)

    Article  Google Scholar 

  3. Long, J., Yu. M., Zhou, M., et al.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011)

    Google Scholar 

  4. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of International Conference on Language Resources and Evaluation (LREc), vol. 10 (2010)

    Google Scholar 

  5. Wen, X., Shao, L., Xue, Y., Fang, W.: A rapid learning algorithm for vehicle classification. Inf. Sci. 295(1), 395–406 (2015)

    Article  Google Scholar 

  6. Chen, B., Shu, H., Coatrieux, G., Chen, G., Sun, X., Coatrieux, J.-L.: Color image analysis by quaternion-type moments. J. Math. Imag. Vis. 51(1), 124–144 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  7. Bin, G., Sheng, V.S., Wang, Z., Ho, D., Osman, S., Li, S.: Incremental learning for - support vector regression. Neural Netw. 67, 140–150 (2015)

    Article  Google Scholar 

  8. Cui, A., Zhang, H., Liu, Y., Zhang, M., Ma, S.: Lexicon-based sentiment analysis on topical chinese microblog messages. In: Li, J., Qi, G., Zhao, D., Nejdl, W., Zheng, H.-T. (eds.) Semantic Web and Web Science, pp. 333–344. Springer, New York (2013)

    Chapter  Google Scholar 

  9. Yuan, B., Liu, Y., Li, H.: Sentiment classification in Chinese microblogs: lexicon-based and learning-based approaches. In: International Proceedings of Economics Development and Research, vol. 68, p. 1 (2013)

    Google Scholar 

  10. Wang, D., Li, F.: Sentiment analysis of Chinese microblogs based on layered features. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8835, pp. 361–368. Springer, Heidelberg (2014). doi:10.1007/978-3-319-12640-1_44

    Google Scholar 

  11. Lee, H.Y., Renganathan, H.: Chinese sentiment analysis using maximum entropy. In: Sentiment Analysis Where AI Meets Psychology (SAAIP), vol. 89 (2011)

    Google Scholar 

  12. Liu, L., Luo, D., Liu, M., Zhong, J., Wei, Y., Sun, L.: A self-adaptive hidden Markov model for emotion classification in Chinese microblogs. Math. Probl. Eng. 2015, 1–8 (2015). doi:10.1155/2015/987189. Article ID 987189

    Google Scholar 

  13. Li, J., Hovy, E.H.: Sentiment analysis on the people’s daily. In: EMNLP, pp. 467–476 (2014)

    Google Scholar 

  14. Bengio, Y., LeCun, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  15. Bengio, S., Vinyals, O., Jaitly, N., et al.: Scheduled sampling for sequence prediction with recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1171–1179 (2015)

    Google Scholar 

  16. Wang, D., Nyberg, E.: A long short-term memory model for answer sentence selection in question answering. In: ACL, July 2015

    Google Scholar 

  17. Socher, R., Perelygin, A., Wu, J.Y., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1631–1642 (2013)

    Google Scholar 

  18. Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075 (2015)

  19. Graves, A.: Supervised sequence labelling. In: Graves, A. (ed.) Supervised Sequence Labelling with Recurrent Neural Networks. SCI, vol. 385, pp. 5–13. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  21. Graves, A., Jaitly, N., Mohamed, A.-R.: Hybrid speech recognition with dee bidirectional LSTM. In: IEEE Workshop on Automatic Speech Recognition and Under-Standing (ASRU), pp. 273–278 (2013)

    Google Scholar 

  22. Hochreiter, S.: The vanishing gradient problem during learning recurrent neural nets, problem solutions. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 6(02), 107–116 (1998)

    Article  MATH  Google Scholar 

  23. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)

    Article  Google Scholar 

  24. Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2003)

    MathSciNet  MATH  Google Scholar 

  25. Zaremba, W., Sutskever, I.: Learning to execute. arXiv preprint arXiv:1410.4615 (2014)

  26. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119 (2013)

    Google Scholar 

  27. Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of HLT-NAACL, pp. 746–751 (2013)

    Google Scholar 

  28. Liu, X., Duh, K., Matsumoto, Y., et al.: Learning character representations for Chinese word segmentation. In: NIPS Workshop on Modern Machine Learning and Natural Language Processing (2014)

    Google Scholar 

  29. Williams, R.J., Zipser, D.: Gradient-based learning algorithms for recurrent networks, their computational complexity. In: Back-Propagation: Theory, Architectures and Applications, pp. 433–486 (1995)

    Google Scholar 

  30. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning, stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)

    MathSciNet  MATH  Google Scholar 

  31. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

Download references

Acknowledgment

The authors thank the anonymous reviewers for their valuable comments and suggestions. The research was partially funded by the National Science Foundation of China (Grant No. 61370095, 61502165), the Open Foundation of Jiangsu Engineering Center of Network Monitoring No. KJR1541, CICAEET fund and PAPD fund, the Scientific Research Fund of Hunan Provincial Education Department (Grant No. 14C0680), and Hunan Normal University of Youth Science Foundation (Grant No. 11404).

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Hunan University, Hunan, China

    Zheng Xiao & PiJun Liang

Authors
  1. Zheng Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. PiJun Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zheng Xiao .

Editor information

Editors and Affiliations

  1. University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Michigan State University , EAST LANSING, Michigan, USA

    Alex Liu

  3. National Dong Hwa University , Shoufeng, Taiwan

    Han-Chieh Chao

  4. Purdue University , West Lafayette, Indiana, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Xiao, Z., Liang, P. (2016). Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48674-1_53

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48673-4

  • Online ISBN: 978-3-319-48674-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation