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Ensemble of Binary Classification for the Emotion Detection in Code-Switching Text

  • Xinghua ZhangEmail author
  • Chunyue ZhangEmail author
  • Huaxing ShiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)

Abstract

This paper describes the methods for the DeepIntell who participated the task1 in the NLPCC2018. The task1 is to label the emotion in a code-switching text. Note that, there may be more than one emotion in a post in this task. Hence, the assessment task is a multi-label classification task. At the same time, the post contains more than one language, and the emotion can be expressed by either monolingual or bilingual form. In this paper, we propose a novel method of converting multi-label classification into binary classification task and ensemble learning for code-switching text with sampling and emotion lexicon. Experiments show that the proposed method has achieved better performance in the code-switching text task.

Keywords

Multi-label classification Binary classification Sampling Emotion lexicon Ensemble learning 

References

  1. 1.
    Lee, S., Wang, Z.: Emotion in code-switching texts: corpus construction and analysis. In: Proceeding of SIGHAN-2015 (2015)Google Scholar
  2. 2.
    Wang, Z., Zhang, Y., Lee, S., Li, S., Zhou, G.: A bilingual attention network for code-switched emotion prediction. In: Proceeding of COLING-2016 (2016)Google Scholar
  3. 3.
    Mostafa, M.M.: More than words: social networks’ text mining for consumer brand sentiments. Expert Syst. Appl. 40(10), 4241–4251 (2013)CrossRefGoogle Scholar
  4. 4.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995).  https://doi.org/10.1007/978-1-4757-3264-1CrossRefzbMATHGoogle Scholar
  5. 5.
    Gao, W., Li, S., Lee, S.Y.M., Zhou, G., Huang, C.R.: Joint learning on sentiment and emotion classification. In: Proceedings of CIKM 2013 (2013)Google Scholar
  6. 6.
    Lin, K., Yang, C., Chen, H.: Emotion classification of online news articles from the reader’s perspective. In: Proceeding of the International Conference on Web Intelligence and Intelligent Agent Technology, pp. 220–226 (2008)Google Scholar
  7. 7.
    Chen, Y., Lee, S., Li, S., Huang, C.: Emotion cause detection with linguistic constructions. In: Proceedings of COLING-10, pp. 179–187 (2010)Google Scholar
  8. 8.
    Xu, G., Meng, X., Wang, H.: Build Chinese emotion lexicons using a graph-based algorithm and multiple resources. In: Proceeding of COLING-2010, pp. 1209–1217 (2010)Google Scholar
  9. 9.
    Volkova, S., Dolan, W., Wilson, T.: CLex: a lexicon for exploring color, concept and emotion associations in language. In: Proceedings of EACL 2012, pp. 306–314 (2012)Google Scholar
  10. 10.
    Alm, C., Roth, D., Sproat, R.: Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of EMNLP, pp. 579–586 (2005)Google Scholar
  11. 11.
    Aman, S., Szpakowicz, S.: Identifying expressions of emotion in text. In: Matoušek, V., Mautner, P. (eds.) TSD 2007. LNCS (LNAI), vol. 4629, pp. 196–205. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74628-7_27CrossRefGoogle Scholar
  12. 12.
    Purver, M., Lee, S., Li, S., Huang, C.: Emotion cause detection with linguistic constructions. In: Proceedings of COLING-2010, pp. 179–187 (2010)Google Scholar
  13. 13.
    Ling, W., Xiang, G., Dyer, C., Black, A., Trancoso, I.: Microblogs as parallel corpora. In: Proceedings of ACL-2013 (2013)Google Scholar
  14. 14.
    Solorio, T., Liu, Y.: Learning to predict code-switching points. In: Proceedings of EMNLP 2008 (2008)Google Scholar
  15. 15.
    Lignos, C., Marcus, M.: Toward web-scale analysis of codeswitching. In: Proceedings of Annual Meeting of the Linguistic Society of America (2013)Google Scholar
  16. 16.
    Li, Y., Fung, P.: Code-switch language model with inversion constraints for mixed language speech recognition. In: Proceedings of COLING-2012 (2012)Google Scholar
  17. 17.
    Peng N., Wang, Y., Dredze, M.: Learning polylingual topic models from code-switched social media documents. In: Proceedings of ACL14 (2014)Google Scholar
  18. 18.
    Yan, Y., Liu, Y., Shyu, M.L., et al.: Utilizing concept correlations for effective imbalanced data classification. In: IEEE International Conference on Information Reuse and Integration, pp. 561–568. IEEE (2014)Google Scholar
  19. 19.
    Wang, Z., Lee, S.Y.M., Li, S., Zhou, G.: Emotion detection in code-switching texts via bilingual and sentimental information. In: Proceeding of ACL-2015, short paper, pp. 763–768 (2015)Google Scholar
  20. 20.
    Kim, Y.: Convolutional Neural Networks for Sentence Classification[J]. Eprint Arxiv (2014)Google Scholar
  21. 21.
    Joulin, A., Grave, E., Bojanowski, P., et al.: Bag of tricks for efficient text classification, pp. 427–431 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.DeepIntell Co. Ltd.HarbinChina

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