Earticle

초록

영어

This work proposes a semi-supervised sentiment classification method which is based on the co-training framework. The proposed method needs to construct three sentiment classifiers. We use common text features to construct the first classifier. We extract opinion words from consumer reviews, and then we ranked these opinion words according to their importance. We also employ extracted opinion words and the ranked co-occurrence opinion words of the extracted opinion words of each review to get the second sentiment classifier. A third sentiment classifier comes into being using non-opinion text features from each review. Based on co-training semi-supervised learning framework, we use the three sentiment classifiers to iteratively get the final sentiment classifier. Experimental results show that our proposed method has better performance than the Self-learning SVM method and the Naive co-training SVM method.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Proposed Approach
  3.1. Introduction to Our Method
  3.2. Opinion Word Extraction
  3.3. Co-occurrence Graph Construction and Opinion Word Ranking
  3.4. Using Ranked Opinion Words and Co-training Framework to Conduct Sentiment Classification
 4. Experiments
  4.1. Experimental Data
  4.2. Compared Methods
  4.3. Experiments
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Suke Li [ School of Software and Microelectronics, Peking University ]
• Yanbing Jiang [ School of Software and Microelectronics, Peking University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605