Ensemble of Neural Networks with Sentiment Words Translation for Code-Switching Emotion Detection

  • Tianchi Yue
  • Chen Chen
  • Shaowu ZhangEmail author
  • Hongfei Lin
  • Liang Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)


Emotion detection in code-switching texts aims to identify the emotion labels of text which contains more than one language. The difficulties of this task include problems in bridging the gap between languages and capturing crucial semantic information for classification. To address these issues, we propose an ensemble model with sentiment words translation to build a powerful system. Our system first constructs an English-Chinese sentiment dictionary to make a connection between two languages. Afterwards, we separately train several models include CNN, RCNN and Attention based LSTM model. Then combine their classification results to improve the performance. The experiment result shows that our method has a good effect and achieves the second place among nineteen systems.


Emotion detection Code-switching Neural networks Sentiment words translation 



This work is supported by National Natural Science Foundation of China (61562080, 61632011, 61572102, 61702080).


  1. 1.
    Lee, S., Wang, Z.: Emotion in code-switching texts: corpus construction and analysis. In: Eighth Sighan Workshop on Chinese Language Processing, pp. 91–99 (2015)Google Scholar
  2. 2.
    Wang, Z., Lee, S.Y.M., Li, S., et al.: Emotion analysis in code-switching text with joint factor graph model. IEEE/ACM Trans. Audio Speech Lang. Process. 25(3), 469–480 (2017)CrossRefGoogle Scholar
  3. 3.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Isa-belle, P. (ed.) Proceedings of the EMNLP 2002, pp. 79–86. ACL, Morristown (2002)Google Scholar
  4. 4.
    Yang, M., Zhu, D., Chow, K-P.: A topic model for building fine-grained domain-specific emotion lexicon. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, 22–27 June 2014, Baltimore, MD, USA, vol. 2: Short Papers, pp. 421–426 (2014)Google Scholar
  5. 5.
    Li, S., Huang, L., Wang, R., Zhou, G.: Sentence-level emotion classification with label and context dependence. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, 26–31 July 2015, Beijing, China, vol. 1: Long Papers, pp. 1045–1053 (2015)Google Scholar
  6. 6.
    Abdul-Mageed, M., Ungar, L.: EmoNet: fine-grained emotion detection with gated recurrent neural networks. In: Meeting of the Association for Computational Linguistics, pp. 718–728 (2017)Google Scholar
  7. 7.
    Zhou, H., Chen, L., Shi, F., Huang, D.: Learning bilingual sentiment word embeddings for cross-language sentiment classification. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2015) (2015)Google Scholar
  8. 8.
    Ling, W., Xiang, G., Dyer, C., Black, A.W., Trancoso, I.: Microblogs as parallel corpora. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, 4–9 August 2013, Sofia, Bulgaria, vol. 1: Long Papers, pp. 176–186 (2013)Google Scholar
  9. 9.
    Wang, Z., Lee, S., Li, S., et al.: Emotion detection in code-switching texts via bilingual and sentimental information. In: Meeting of the Association for Computational Linguistics and the, International Joint Conference on Natural Language Processing, pp. 763–768 (2015)Google Scholar
  10. 10.
    Wang, Z., Yue, Z., et al.: A bilingual attention network for code-switched emotion prediction. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics, pp. 1624–1634 (2016)Google Scholar
  11. 11.
    Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Disc. (2018)Google Scholar
  12. 12.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Empirical Methods in Natural Language Processing, pp. 1746–1751 (2014)Google Scholar
  13. 13.
    Lai, S., Xu, L., Liu, K., et al.: Recurrent convolutional neural networks for text classification. In: National Conference on Artificial Intelligence, pp. 2267–2273 (2015)Google Scholar
  14. 14.
    Yang, Z., Yang, D., Dyer, C., et al.: Hierarchical attention networks for document classification. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)Google Scholar
  15. 15.
    Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. Comput. Sci. (2013)Google Scholar
  16. 16.
    Liu, B., Hu, M., Cheng, J., Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th International World Wide Web conference (WWW-2005), Chiba, Japan, pp. 10–14 (2005)Google Scholar
  17. 17.
    Graves, A.: Long short-term memory. In: Graves, A. (ed.) Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence, vol. 385. Springer, Heidelberg (1997)zbMATHGoogle Scholar
  18. 18.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tianchi Yue
    • 1
  • Chen Chen
    • 1
  • Shaowu Zhang
    • 1
    Email author
  • Hongfei Lin
    • 1
  • Liang Yang
    • 1
  1. 1.Dalian University of TechnologyDalianChina

Personalised recommendations