Abstract
Sentiment connection is the basis of cross-lingual sentiment analysis (CSLA) solutions. Most of existing work mainly focus on general semantic connection that the misleading information caused by non-sentimental semantics probably lead to relatively low efficiency. In this paper, we propose to capture the document-level sentiment connection across languages (called cross-lingual sentiment relation) for CLSA in a joint two-view convolutional neural networks (CNNs), namely Bi-View CNN (BiVCNN). Inspired by relation embedding learning, we first project the extracted parallel sentiments into a bilingual sentiment relation space, then capture the relation by subtracting them with an error-tolerance. The bilingual sentiment relation considered in this paper is the shared sentiment polarity between two parallel texts. Experiments conducted on public datasets demonstrate the effectiveness and efficiency of the proposed approach.
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
Notes
- 1.
NLP&CC is an annual conference of Chinese information technology professional committee organized by Chinese Computer Federation (CCF). For more details, please refer to: http://tcci.ccf.org.cn/conference/2013/dldoc/evdata03.zip.
- 2.
Word2Vec is one of the models implemented in the free python library Gensim: http://pypi.python.org/pypi/gensim.
- 3.
The pre-trained word embedding models and the Weibo posts leveraged are available at: https://drive.google.com/open?id=0B0l0oLL2GUuoblNta0QyY1BkdGM.
- 4.
The parameter setting used in this paper is ‘-s 7’.
References
Bordes, A., Glorot, X., Weston, J., Bengio, Y.: Joint learning of words and meaning representations for open-text semantic parsing. In: International Conference on Artificial Intelligence and Statistics, pp. 127–135 (2012)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94, 233–259 (2014)
Chen, Q., Li, W., Lei, Y., Liu, X., He, Y.: Learning to adapt credible knowledge in cross-lingual sentiment analysis. In: Proceedings of the 53rd Annual Meetings of the Association for Computational Linguistics, pp. 419–429. Association for Computational Linguistics (2015)
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: A library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008). JMLR.org
Gui, L., Xu, R., Lu, Q., Xu, J., Xu, J., Liu, B., Wang, X.: Cross-lingual opinion analysis via negative transfer detection. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 23–25 June, Baltimore, Maryland, USA, pp. 860–865. Association for Computational Linguistics (2014)
He, Y.: Latent sentiment model for weakly-supervised cross-lingual sentiment classification. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 214–225. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20161-5_22
Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.R.: A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing System, pp. 3167–3475 (2012)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI 2015, pp. 716–717 (2015)
Liu, B.: Sentiment analysis and opinion mining. In: Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167. Morgan & Claypool Publishers (2012)
McAuley, J., Targett, C., Shi, Q., van den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of SIGIR 2015, 09–13 August 2015, Santiago, Chile (2015)
Meng, X., Wei, F., Liu, X., Zhou, M., Xu, G., Wang, H.: Cross-lingual mixture model for sentiment classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pp. 572–581. Association for Computational Linguistics (2012)
Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751 (2013)
Popat, K., Balamurali, A.R.: The haves and the have-nots: leveraging unlabelled corpora for sentiment analysis. Citeseer (2013)
Prettenhofer, P., Stein, B.: Cross-language text classification using structural correspondence learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1118–1127. Association for Computational Linguistics (2010)
Rothe, S., Ebert, S., Schütze, H.: Ultradense word embeddings by orthogonal transformation. arXiv preprint arXiv:1602.07572 (2016)
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934 (2013)
Sutskever, I., Tenenbaum, J.B., Salakhutdinov, R.B.: Modelling relational data using bayesian clustered tensor factorization. In: Advances in Neural Information Processing Systems, pp. 1821–1828 (2009)
Tang, X., Wan, X.: Learning bilingual embedding model for cross-language sentiment classification. In: Proceedings of 2014 IEEE/WIA/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), pp. 134–141 (2014)
Wan, X.: 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: Volume 1-Volume 1, pp. 235–243. Association for Computational Linguistics (2009)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1112–1119. Citeseer (2014)
Xiao, M., Guo, Y.: A novel two-step method for cross language representation learning. In: Advances in Neural Information Processing Systems, pp. 1259–1267 (2013)
Zhang, H., Yu, H., Xiong, D., Liu, Q.: HHMM-based Chinese lexical analyzer ICTCLAS. In: 2nd SIGHAN Workshop Affiliated with 42nd ACL, pp. 430–440 (2003)
Zhou, H., Chen, L., Huang, D.: Cross-lingual sentiment classification based on denoising autoencoder. In: Zong, C., Nie, J.Y., Zhao, D., Feng, Y. (eds.) Natural Language Processing and Chinese Computing. CCIS, vol. 496, pp. 181–192. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45924-9_17
Zhou, G., He, T., Zhao, J., Wu, W.: A subspace learning framework for cross-lingual sentiment classification with partial parallel data. In: Proceedings of the International Joint Conference on Artificial Intelligence, Buenos Aires (2015a)
Zhou, H., Chen, L., Shi, F., Huang, D.: Learning bilingual sentiment word embeddings for cross-language sentiment classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pp. 430–440. Association for Computational Linguistics (2015b)
Acknowledge
We thank all the anonymous reviewers for their detailed and insightful comments on this paper. The work described in this paper was supported by National Natural Science Foundation of China (61272291, 61672445, 61472290 and 61472291) and The Hong Kong Polytechnic University (G-YBP6, 4-BCB5 and B-Q46C).
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
Chen, Q., Li, W., Lei, Y., Liu, X., Luo, C., He, Y. (2017). Cross-Lingual Sentiment Relation Capturing for Cross-Lingual Sentiment Analysis. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-56608-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56607-8
Online ISBN: 978-3-319-56608-5
eBook Packages: Computer ScienceComputer Science (R0)