Deep Learning Model for Sentiment Analysis in Multi-lingual Corpus

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)


While most text classification studies focus on monolingual documents, in this article, we propose an empirical study of poly-languages text sentiment classification model, based on Convolutional Networks ConvNets. The novel approach consists on feeding the deep neural network with one input text source composed by reviews all written in different languages, without any code-switching indication, or language translation. We construct a multi-lingual opinion corpus combining three languages: English French and Greek all from Restaurants Reviews. Despite the limited contextual information due to relatively compact text content, no prior knowledge is used. The neural networks exploit n-gram level information, and the experimental results achieve high accuracy for sentiment polarity prediction, both positive and negative, which lead us to deduce that ConvNets features extraction is language independent.


Deep learning Opinion mining Sentiment analysis ConvNets 


  1. 1.
    Turney, P.: Thumbs Up or Thumbs Down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Stroudsburg, pp. 417–424 (2002)Google Scholar
  2. 2.
    Efron, M.: Cultural orientations: classifying subjective documents by cocitation analysis. In: Proceedings of the AAAI Fall Symposium Series on Style and Meaning in Language, Art, Music, and Design, pp. 41–48 (2004)Google Scholar
  3. 3.
    Wiebe, J., Bruce, T., Bell, R., Martin, M.: Learning subjective language. Comput. Linguist. 30(3), 277–308 (2004)CrossRefGoogle Scholar
  4. 4.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics, Stroudsburg (2002)Google Scholar
  5. 5.
    Pennington, J., Socher, R., Manning, D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  6. 6.
    Dos Santos, N., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, pp. 69–78 (2014)Google Scholar
  7. 7.
    Vilares, D., Alonso, M., Gomez-Rodriguez, C.: Supervised sentiment analysis in multilingual environments. In: Information Processing & Management (2017).
  8. 8.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)MATHGoogle Scholar
  9. 9.
    Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: ACL - Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, pp. 655–665, April 2014Google Scholar
  10. 10.
    Garcia-Sierra, A., Rivera-Gaxiola, M., Conboy, B., Romo, H., Klarman, L., Ortiz, S., Kuhl, P.: Bilingual language learning: an ERP study relating early brain responses to speech, language input, and later word production. J. Phonetics 39(4), 546–557 (2011)CrossRefGoogle Scholar
  11. 11.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Empirical Methods in Natural Language Processing, pp. 1746–1751, August 2014Google Scholar
  12. 12.
    Ruder, S., Ghaffari, P., Breslin, J.: Deep Learning for Multilingual Aspect-based Sentiment Analysis. IN: INSIGHT-1 at SemEval-2016 Task 5 (2016)Google Scholar
  13. 13.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference for Learning Representations (2015)Google Scholar
  14. 14.
    Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. In: CoRR (2012)Google Scholar
  15. 15.
    Byers-Heinlein, K., Lew-Williams, C.: Bilingualism in the early years what the science says. LEARNing Landscapes 7(1), 95–112 (2013)Google Scholar
  16. 16.
    Severyn, A., Moschitti, A.: August). Twitter sentiment analysis with deep convolutional neural networks. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 959–962 (2015)Google Scholar
  17. 17.
    Arkhipenko, K., Kozlov, I., Trofimovich, J., Skorniakov, K., Gomzin, A., Turdakov, D.: Comparison of neural network architectures for sentiment analysis of russian tweets. In: Computational Linguistics and Intellectual Technologies, Proceedings of the International Conference Dialogue (2016)Google Scholar
  18. 18.
    Chollet, F.: Keras. In: GitHub (2015).
  19. 19.
    Bing, L.: Sentiment analysis and opinion mining. In: Morgan and Claypool (2012)Google Scholar
  20. 20.
    Denecke, K.: Using SentiWordNet for multilingual sentiment analysis. In: 2008 IEEE 24th International Conference on Data Engineering Workshop (2008)Google Scholar
  21. 21.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25, 1097–1105 (2012)Google Scholar
  22. 22.
    Sallab, A., Baly, R., El Hajj, W., Shaban, K.: Deep learning models for sentiment analysis in Arabic. In: Arabic NLP workshop, ACL-IJCNLP, The 53rd Annual Meeting of the Association for Computational Linguistics and The 7th International Joint Conference of the Asian Federation of Natural Language Processing, Beijing, China (2015)Google Scholar
  23. 23.
    Wang, B., Liu, M.: Deep learning for aspect-based sentiment analysis. In: DeepLF (2015)Google Scholar
  24. 24.
    Irsoy, O., Cardie, C.: Opinion mining with deep recurrent neural networks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 720–728 (2014)Google Scholar
  25. 25.
    Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 151–161 (2011)Google Scholar
  26. 26.
    Socher, R., Perelygin, A., Wu, A., Chuang, J., Manning, C., NG, A., Potts, C., Manning, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)Google Scholar
  27. 27.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  28. 28.
    Xu, L., Liu, K., Lai, S., Zhao, J.: Product feature mining: Semantic clues versus syntactic constituents. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, Maryland, USA, pp. 336–346, June 2014Google Scholar
  29. 29.
    Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, ICML, New York, pp 160–167 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.LIASD, Université Paris 8Saint-denisFrance

Personalised recommendations