Abstract
Cross-language sentiment classification (CLSC) aims at leveraging the semantic and sentiment knowledge in a resource-abundant language (source language) for sentiment classification in a resource-scarce language (target language). This paper proposes an approach to jointly learning bilingual semantic and sentiment representations (BSSR) for English-Chinese CLSC. First, two neural networks are adopted to learn sentence-level sentiment representations in English and Chinese views respectively, which are attached to all word semantic representations in the corresponding sentence to express the words in the certain sentiment context. Then, another two neural networks in two views are designed to jointly learn BSSR of the document from word representations concatenated with their sentence-level sentiment representations. The proposed approach could capture rich sentiment and semantic information in BSSR learning process. Experiments on NLP&CC 2013 CLSC dataset show that our approach is competitive with the state-of-the-art results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of ACL, USA, pp. 417–424 (2002)
Tang, D.Y., Wei, F.R., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceeding of ACL, USA, pp. 1555–1565 (2014)
Tang, D.Y., Qin, B., Feng, X.C., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceeding of COLING, Japan, pp. 3298–3307 (2016)
Li, S., Wang, R., Liu, H., Huang, C.R.: Active learning for cross-lingual sentiment classification. In: Zhou, G., Li, J., Zhao, D., Feng, Y. (eds.) NLPCC 2013. Communications in Computer and Information Science, vol. 400, pp. 236–246. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41644-6_22
Wan, X.J.: Co-training for cross-lingual sentiment classification. In: Proceedings of ACL, Singapore, pp. 235–243 (2009)
Chen, Q., Li, W.J., Lei, Y., Liu, X.L., He, Y.X.: Learning to adapt credible knowledge in cross-lingual sentiment analysis. In: Proceedings of ACL, China, pp. 419–429 (2015)
Chander, S., Lauly, S., Larochelle, H., et al.: An autoencoder approach to learning bilingual word representations. In: Proceeding of NIPS, Canada, pp. 1853–1861 (2014)
Yoshikawa, Y.Y., Iwata, T., Sawada, H., Yamada, T.: Cross-domain matching for bag-of-words data via kernel embeddings of latent distributions. In: Proceedings of NIPS, Canada, pp. 1405–1413 (2015)
Rajendran, J., Khapra, M., Chandar, S., Ravindran, B.: Bridge correlational neural networks for multilingual multimodal representation learning (2015). arXiv preprint arXiv:1510.03519
Tang, X.W., Wan, X.J.: Learning bilingual embedding model for cross-language sentiment classification. In: the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, pp. 134–141 (2014)
Zhou, H.W., Chen, L., Shi, F.L., Huang, D.G.: Learning bilingual sentiment word embeddings for cross-language sentiment classification. In: Proceedings of ACL, China, pp. 430–440 (2015)
Klementiev, A., Ivan, T., Bhattarai, B.: Inducing cross lingual distributed representations of words. In: Proceedings of COLING, Indiana, pp. 1459–1474 (2012)
Jain, S., Batra, S.: Cross-lingual sentiment analysis using modified BRAE. In: Proceedings of EMNLP, Portugal, pp. 159–168 (2015)
Zhou, X.J., Wan, X.J., Xiao, J.G.: Cross-lingual sentiment classification with bilingual document representation learning. In: Proceedings of ACL, Germany, pp. 1403–1412 (2016)
Zhou, X.J, Wan, X.J and Xiao, J.G.: Attention-based LSTM network for cross-lingual sentiment classification. In: Proceedings of EMNLP, USA, pp. 247–256 (2016)
Collobert, R., Weston, J., Bottou, L., et al.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Wang, X., Liu, Y.C., Sun, C.J., Wang, B.X., Wang, X.L.: Predicting polarities of tweets by composing word embeddings with long short-term memory. In: Proceedings of ACL, China, pp. 1343–1353 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Gers, F.A., Schmidhuber, J.: Recurrent nets that time and count. In: The IEEE-Inns-Enns International Joint Conference on Neural Networks, vol. 3, pp. 189–194 (2000)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. Int. J. Comput. Appl. 7(5), 347–354 (2005)
Xu, L.H., Lin, H.F., Pan, Y., Ren, H., Chen, J.M.: Constructing the affective lexicon ontology. J. China Soc. Sci. Tech. Inf. 27(2), 180–185 (2008)
Bastien, F., Lamblin, P., Pascanu, R., et al.: Theano: new features and speed improvements (2012). arXiv preprint arXiv:1211.5590
Pennington, B., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of EMNLP, USA, pp. 1532–1543 (2014)
Gui, L., Lu, Q., Xu, R., Wei, Q., Cao, Y.: Improving transfer learning in cross lingual opinion analysis through negative transfer detection. In: Zhang, S., Wirsing, M., Zhang, Z. (eds.) KSEM 2015. LNCS, vol. 9403, pp. 394–406. Springer, Cham (2015). doi:10.1007/978-3-319-25159-2_36
Acknowledgements
This research is supported by Natural Science Foundation of China (No. 61272375).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhou, H., Yang, Y., Liu, Z., Lin, Y., Zhu, P., Huang, D. (2017). Jointly Learning Bilingual Sentiment and Semantic Representations for Cross-Language Sentiment Classification. In: Wen, J., Nie, J., Ruan, T., Liu, Y., Qian, T. (eds) Information Retrieval. CCIR 2017. Lecture Notes in Computer Science(), vol 10390. Springer, Cham. https://doi.org/10.1007/978-3-319-68699-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-68699-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68698-1
Online ISBN: 978-3-319-68699-8
eBook Packages: Computer ScienceComputer Science (R0)