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A Joint Prediction Model for Multiple Emotions Analysis in Sentences

  • Yunong Wu
  • Kenji Kita
  • Kazuyuki Matsumoto
  • Xin Kang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7817)

Abstract

In this study, we propose a scheme for recognizing people’s multiple emotions from Chinese sentence. Compared to the previous studies which focused on the single emotion analysis through texts, our work can better reflect people’s inner thoughts by predicting all the possible emotions. We first predict the multiple emotions of words from a CRF model, which avoids the restrictions from traditional emotion lexicons with limited resources and restricted context information. Instead of voting emotions directly, we perform a probabilistic merge of the output words’ multi-emotion distributions to jointly predict the sentence emotions, under the assumption that the emotions from the contained words and a sentence are statistically consistent. As a comparison, we also employ the SVM and LGR classifiers to predict each entry of the multiple emotions through a problem-transformation method. Finally, we combine the joint probabilities of the multiple emotions of sentence generated from the CRF-based merge model and the transformed LGR model, which is proved to be the best recognition for sentence multiple emotions in our experiment.

Keywords

Multiple emotions Joint prediction CRF LGR 

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References

  1. 1.
    Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)Google Scholar
  2. 2.
    Wu, Y., Kita, K., Ren, F., Matsumoto, K., Kang, X.: Exploring EmotionalWords for Chinese Document Chief Emotion Analysis. In: Proc. of 25th PACLIC, pp. 597–606 (2011)Google Scholar
  3. 3.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML, pp. 282–289 (2001)Google Scholar
  4. 4.
    Menard, S.: Applied Logistic Regression Analysis (Sage University Paper Series 07-106). Sage Publications, Thousand Oaks (1995)Google Scholar
  5. 5.
    Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Bermingham, A., Ghose, A., Smeaton, A.F.: Classifying sentiment in microblogs: Is brevity an advantage? In: Proc. of CIKM, pp. 1833–1836. ACM (2010)Google Scholar
  7. 7.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification using Machine Learning. In: Proc. of the Conference on EMNLP, pp. 79–86 (2002)Google Scholar
  8. 8.
    Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proc. of the ACL, pp. 417–424 (July 2002)Google Scholar
  9. 9.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proc. of the LREC 2010 (ELRA), Valletta, Malta, pp. 19–21 (May 2010)Google Scholar
  10. 10.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical report, Stanford Digital Library Technologies Project (2009)Google Scholar
  11. 11.
    Tokuhisa, R., Inui, K., Matsumoto, Y.: Emotion Classification Using Massive Examples Extracted from the Web. In: Proc. of Coling 2008, pp. 881–888 (2008)Google Scholar
  12. 12.
    Alm, C.O., Roth, D., Sproat, R.: Emotions from text: Machine learning for text-based emotion prediction. In: Proc. of HLT/EMNLP (2005)Google Scholar
  13. 13.
    Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: Proceedings of the 2008 ACM symposium on Applied computing, NewYork, pp. 1556–1560 (2008)Google Scholar
  14. 14.
    Kang, X., Ren, F.: Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network. journal of China Communications 9(3), 99–109 (2012)Google Scholar
  15. 15.
    Schapire, R.E., Singer, Y.: BoosTexter: A Boosting-based System for Text Categorization. In: Machine Learning, vol. 39, pp. 135–168 (2000)Google Scholar
  16. 16.
    Godbole, S., Sarawagi, S.: Discriminative Methods for Multi-labeled Classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22–30. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yunong Wu
    • 1
  • Kenji Kita
    • 1
  • Kazuyuki Matsumoto
    • 1
  • Xin Kang
    • 1
  1. 1.Faculty of EngineeringUniversity of TokushimaJapan

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