Learning Sentence Representation for Emotion Classification on Microblogs

  • Duyu Tang
  • Bing Qin
  • Ting Liu
  • Zhenghua Li
Part of the Communications in Computer and Information Science book series (CCIS, volume 400)

Abstract

This paper studies the emotion classification task on microblogs. Given a message, we classify its emotion as happy, sad, angry or surprise. Existing methods mostly use the bag-of-word representation or manually designed features to train supervised or distant supervision models. However, manufacturing feature engines is time-consuming and not enough to capture the complex linguistic phenomena on microblogs. In this study, to overcome the above problems, we utilize pseudo-labeled data, which is extensively explored for distant supervision learning and training language model in Twitter sentiment analysis, to learn the sentence representation through Deep Belief Network algorithm. Experimental results in the supervised learning framework show that using the pseudo-labeled data, the representation learned by Deep Belief Network outperforms the Principal Components Analysis based and Latent Dirichlet Allocation based representations. By incorporating the Deep Belief Network based representation into basic features, the performance is further improved.

Keywords

Emotion Classification Deep Belief Network Representation Learning Microblogs 

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References

  1. 1.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  2. 2.
    Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)CrossRefGoogle Scholar
  3. 3.
    Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the Sixth WSDM, pp. 537–546. ACM (2013)Google Scholar
  4. 4.
    Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proc. 49th ACL: HLT, vol. 1, pp. 151–160 (2011)Google Scholar
  5. 5.
    Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the bad and the omg! In: Fifth International AAAI Conference on Weblogs and Social Media (2011)Google Scholar
  6. 6.
    Yang, C., Lin, K., Chen, H.: Emotion classification using web blog corpora. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 275–278. IEEE (2007)Google Scholar
  7. 7.
    Mishne, G.: Experiments with mood classification in blog posts. In: Proceedings of ACM SIGIR 2005 Workshop on Stylistic Analysis of Text for Information Access, p. 19 (2005)Google Scholar
  8. 8.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1–12 (2009)Google Scholar
  9. 9.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the Conference on EMNLP, pp. 79–86. ACL (2002)Google Scholar
  10. 10.
    Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on Audio, Speech, and Language Processing 20(1), 30–42 (2012)CrossRefGoogle Scholar
  11. 11.
    Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)Google Scholar
  12. 12.
    Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48 (2005)Google Scholar
  13. 13.
    Liu, K., Li, W., Guo, M.: Emoticon smoothed language models for twitter sentiment analysis. In: Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)Google Scholar
  14. 14.
    Li, F., Pan, S.J., Jin, O., Yang, Q., Zhu, X.: Cross-domain co-extraction of sentiment and topic lexicons. In: Proceedings of the 50th ACL, pp. 410–419. ACL (July 2012)Google Scholar
  15. 15.
    Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd COLING Posters, pp. 36–44. ACL (2010)Google Scholar
  16. 16.
    Johansson, R., Moschitti, A.: Extracting opinion expressions and their polarities–exploration of pipelines and joint models. In: Proceedings of ACL, vol. 11 (2011)Google Scholar
  17. 17.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2-3), 165–210 (2005)CrossRefGoogle Scholar
  18. 18.
    Salakhutdinov, R., Hinton, G.: Semantic hashing. International Journal of Approximate Reasoning 50(7), 969–978 (2009)CrossRefGoogle Scholar
  19. 19.
    Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. Advances in Neural Information Processing Systems 19, 153 (2007)Google Scholar
  21. 21.
    Hinton, G.: Training products of experts by minimizing contrastive divergence. Neural Computation 14(8), 1771–1800 (2002)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24(6), 417 (1933)CrossRefGoogle Scholar
  23. 23.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)MATHGoogle Scholar
  24. 24.
    Turney, P.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th ACL, pp. 417–424. ACL (2002)Google Scholar
  25. 25.
    Mishne, G., De Rijke, M.: Capturing global mood levels using blog posts. In: AAAI 2006 Spring Symposium on Computational Approaches to Analysing Weblogs, pp. 145–152 (2006)Google Scholar
  26. 26.
    Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd COLING: Posters, pp. 241–249. ACL (2010)Google Scholar
  27. 27.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. arXiv preprint arXiv:1206.5538 (2012)Google Scholar
  28. 28.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. JMLR 12, 2493–2537 (2011)Google Scholar
  29. 29.
    Chen, M., Xu, Z., Weinberger, K., Sha, F.: Marginalized denoising autoencoders for domain adaptation. In: ICML (2012)Google Scholar
  30. 30.
    Socher, R., Bauer, J., Manning, C.D., Ng, A.Y.: Parsing with compositional vector grammars. In: ACL (2013)Google Scholar
  31. 31.
    He, Z., Liu, S., Li, M., Zhou, M., Zhang, L., Wang, H.: Learning entity representation for entity disambiguation. In: ACL (2013)Google Scholar
  32. 32.
    Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. Urbana 51, 61801 (2010)Google Scholar
  33. 33.
    Socher, R., Pennington, J., Huang, E., Ng, A., Manning, C.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: EMNLP, pp. 151–161 (2011)Google Scholar
  34. 34.
    Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic Compositionality Through Recursive Matrix-Vector Spaces. In: Proceedings of the 2012 Conference on EMNLP (2012)Google Scholar
  35. 35.
    Maas, A.L., Daly, R., Pham, P., Huang, D., Ng, A., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th ACL, ACL 2011 (2011)Google Scholar
  36. 36.
    Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: A deep learning approach. In: Proceedings of ICML (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Duyu Tang
    • 1
  • Bing Qin
    • 1
  • Ting Liu
    • 1
  • Zhenghua Li
    • 1
  1. 1.Research Center for Social Computing and Information RetrievalHarbin Institute of TechnologyHarbinChina

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