A weakly supervised approach to Chinese sentiment classification using partitioned self-training

Journal of Information Science

Volumn 39, Issue 6, 2013, Pages 815-831

  Zhang, Pu ^a,b   He, Zhongshi ^b  

^a CHONGQING UNIVERSITY   (China)

^b CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

opinion mining; self training; sentiment classification; weakly supervised

Indexed keywords

CLASSIFICATION RESULTS; COMPETITIVE ADVANTAGE; OPINION MINING; SELF-TRAINING; SENTIMENT ANALYSIS; SENTIMENT CLASSIFICATION; TRAINING SETS; WEAKLY SUPERVISED;

COMPETITION; DATA MINING; STATISTICAL TESTS; WORLD WIDE WEB;

CLASSIFICATION (OF INFORMATION);

EID: 84887569921     PISSN: 01655515     EISSN: 17416485     Source Type: Journal    
DOI: 10.1177/0165551513480330     Document Type: Article

References (29)

1. Liu B Handbook of natural language processing. 2 nd edn.New York: Chapman and Hall, 2010, p. 629. Indurkhya N Damerau FJ, ed. New York: Chapman and Hall ; 2010: 629.
2. Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. Conference on empirical methods in natural language processing (EMNLP) ; 2002 Philadelphia, PA ; 2002. 79.
3. Aue A, Gamon M. Customizing sentiment classifiers to new domains: A case study. Proceedings of recent advances in natural language processing (RANLP). 2005 ;: 207-218
4. Turney PD. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. 40th annual meeting of the Association for Computational Linguistics (ACL) ; 2002 Philadelphia, PA ; 2002. 417.
5. Melville P, Gryc W, Lawrence R. Sentiment analysis of blogs by combining lexical knowledge with text classification. Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. 2009 ;: 1275-1284
6. Tan S, Wang Y, Cheng X. Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples. Proceedings of the SIGIR. 2008 ;: 743-744
7. Li T, Zhang Y, Sindhwani V. A non-negative matrix tri-factorization approach to sentiment classification with lexical prior knowledge. Proceedings of the joint conference of the annual meeting of the association for computational linguistics and the international joint conference on natural language processing of the asian federation of natural language processing (ACL-IJCNLP). 2009 ;: 244-252
8. Andreevskaia A, Bergler S. When specialists and generalists work together: Overcoming domain dependence in sentiment tagging. Proceedings of the association for computational linguistics and the human language technology conference (ACL-HLT). 2008 ;: 290-298
9. Zagibalov T, Carroll J. Unsupervised classification of sentiment and objectivity in Chinese text. Proceedings of the IJCNLP. 2008 ;: 304-311
10. Zagibalov T, Carroll J. Automatic seed word selection for unsupervised sentiment classification of Chinese text. Proceedings of the 22nd international conference on computational linguistics (COLING). 2008 ;: 1073-1080
11. Qiu L, Zhang W, Hu C, Zhao K. SELC: A self-supervised model for sentiment classification. Proceeding of the 18th ACM conference on information and knowledge management (CIKM). 2009 ;: 929-936
12. Li S, Huang CR, Zhou G, Lee S. Employing personal/impersonal views in supervised and semi-supervised sentiment classification. Proceedings of the 48th annual meeting of the association for computational linguistics. 2010 ;: 414-423
13. Abbasi A, France S, Zhang Z, Chen H. Selecting attributes for sentiment classification using feature relation networks. IEEE Transactions on Knowledge and Data Engineering. 2011 ; 23 (3). 447-462
14. Xia R, Zong C, Li S. Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences. 2011 ; 181: 1138-1152
15. Thet TT, Na J-C, Khoo SGC. Aspect-based sentiment analysis of movie reviews on discussion boards. Journal of Information Science. 2010 ; 36 (6). 823-848
16. Riloff E, Patwardhan S, Wiebe J. Feature subsumption for opinion analysis. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). 2006 ;: 440-448
17. Zhai Z, Xu H, Kang B, Jia P. Exploiting effective features for Chinese sentiment classification. Expert Systems with Applications. 2011 ; 38 (8). 9139-9146
18. Sindhwani V, Melville P. Document-word co-regularization for semi-supervised sentiment analysis. Proceedings of ICDM. 2008 ;: 1025-1030
19. Li S, Wang Z, Zhou G, Lee S. Semi-supervised learning for imbalanced sentiment classification. Proceedings of IJCAI. 2011 ;: 826-1831
20. Wan X. Bilingual co-training for sentiment classification of Chinese product reviews. Computational Linguistics. 2011 ; 37 (3). 587-616
21. Kennedy A, Inkpen D. Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence. 2006 ; 22 (2). 110-125
22. Zhang C, Zeng D, Li J, Wang F, et al. Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology. 2009 ; 60 (12). 2474-2487
23. Wan X. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. Proceedings of the conference on empirical methods in natural language processing (EMNLP). 2008 ;: 553-561
24. He Y, Zhou D. Self-training from labelled features for sentiment analysis. Information Processing and Management. 2011 ; 47 (4). 606-616
25. Wiegand M, Klakow D. Bootstrapping supervised machine-learning polarity classifiers with rule-based classification. Proceedings of 1st workshop on computational approaches to subjectivity and sentiment analysis (WASSA). 2010 ;:
26. Wang D, Liu Y. A cross-corpus study of subjectivity identification using unsupervised learning. Natural Language Engineering. 2012 ; 18 (3). 375-397
27. Wiebe J, Riloff E. Creating subjective and objective sentence classifiers from unannotated texts. Proceedings of CICLing. 2005 ;:
28. Li F, Huang M, Yang Y, Zhu X. Learning to identify review spam. Proceedings of the twenty-second international joint conference on artificial intelligence (IJCAI). 2011 ;:
29. Li J, Sun M. Experimental study on sentiment classification of Chinese review using machine learning techniques. Proceedings of IEEE International Conference on NLPKE. 2007 ;: