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)


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.


Multiple emotions Joint prediction CRF LGR 


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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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