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2016 | OriginalPaper | Buchkapitel

Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding

verfasst von : Zheng Xiao, PiJun Liang

Erschienen in: Cloud Computing and Security

Verlag: Springer International Publishing

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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.

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Literatur
1.
Zurück zum Zitat Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, pp. 627–666 (2010) Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, pp. 627–666 (2010)
2.
Zurück zum Zitat Balahur, A., Steinberger, R., Kabadjov, M.: Sentiment analysis in the news. Infrared Phys. Technol. 65, 94–102 (2014)CrossRef Balahur, A., Steinberger, R., Kabadjov, M.: Sentiment analysis in the news. Infrared Phys. Technol. 65, 94–102 (2014)CrossRef
3.
Zurück zum Zitat 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) 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)
4.
Zurück zum Zitat 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) 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)
5.
Zurück zum Zitat Wen, X., Shao, L., Xue, Y., Fang, W.: A rapid learning algorithm for vehicle classification. Inf. Sci. 295(1), 395–406 (2015)CrossRef Wen, X., Shao, L., Xue, Y., Fang, W.: A rapid learning algorithm for vehicle classification. Inf. Sci. 295(1), 395–406 (2015)CrossRef
6.
Zurück zum Zitat 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)MathSciNetCrossRefMATH 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)MathSciNetCrossRefMATH
7.
Zurück zum Zitat 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)CrossRef 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)CrossRef
8.
Zurück zum Zitat 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)CrossRef 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)CrossRef
9.
Zurück zum Zitat 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) 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)
10.
Zurück zum Zitat 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 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
11.
Zurück zum Zitat Lee, H.Y., Renganathan, H.: Chinese sentiment analysis using maximum entropy. In: Sentiment Analysis Where AI Meets Psychology (SAAIP), vol. 89 (2011) Lee, H.Y., Renganathan, H.: Chinese sentiment analysis using maximum entropy. In: Sentiment Analysis Where AI Meets Psychology (SAAIP), vol. 89 (2011)
12.
Zurück zum Zitat 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 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
13.
Zurück zum Zitat Li, J., Hovy, E.H.: Sentiment analysis on the people’s daily. In: EMNLP, pp. 467–476 (2014) Li, J., Hovy, E.H.: Sentiment analysis on the people’s daily. In: EMNLP, pp. 467–476 (2014)
14.
Zurück zum Zitat Bengio, Y., LeCun, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRef Bengio, Y., LeCun, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRef
15.
Zurück zum Zitat 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) 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)
16.
Zurück zum Zitat Wang, D., Nyberg, E.: A long short-term memory model for answer sentence selection in question answering. In: ACL, July 2015 Wang, D., Nyberg, E.: A long short-term memory model for answer sentence selection in question answering. In: ACL, July 2015
17.
Zurück zum Zitat 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) 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)
18.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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)CrossRef 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)CrossRef
20.
Zurück zum Zitat Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
21.
Zurück zum Zitat 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) 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)
22.
Zurück zum Zitat 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)CrossRefMATH 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)CrossRefMATH
23.
Zurück zum Zitat Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)CrossRef Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)CrossRef
24.
Zurück zum Zitat Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2003)MathSciNetMATH Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2003)MathSciNetMATH
26.
Zurück zum Zitat 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) 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)
27.
Zurück zum Zitat Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of HLT-NAACL, pp. 746–751 (2013) Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of HLT-NAACL, pp. 746–751 (2013)
28.
Zurück zum Zitat 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) 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)
29.
Zurück zum Zitat 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) 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)
30.
Zurück zum Zitat Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning, stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning, stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH
31.
Zurück zum Zitat 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) 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)
Metadaten
Titel
Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding
verfasst von
Zheng Xiao
PiJun Liang
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-48674-1_53