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Exploiting Co-occurrence Opinion Words for Semi-supervised Sentiment Classification

  • Suke Li
  • Jinmei Hao
  • Yanbing Jiang
  • Qi Jing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)

Abstract

This work proposes a semi-sentiment classification method by exploiting co-occurrence opinion words. Our method is based on the observation that opinion words with similar sentiment have high possibility to co-occur with each other. We show co-occurrence opinion words are helpful for improving sentiment classification accuracy. We employ the co-training framework to conduct semi-supervised sentiment classification. Experimental results show that our proposed method has better performance than the Self-learning SVM method.

Keywords

Opinion Mining Sentiment Analysis Unlabeled Data Training Instance Computational Linguistics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Suke Li
    • 1
  • Jinmei Hao
    • 2
  • Yanbing Jiang
    • 1
  • Qi Jing
    • 1
  1. 1.School of Software and MicroelectronicsPeking UniversityChina
  2. 2.Beijing Union UniversityChina

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