Semi-stacking for semi-supervised sentiment classification

ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference

Volumn 2, Issue , 2015, Pages 27-31

  Li, Shoushan ^a,b   Huang, Lei ^a   Wang, Jingjing ^a   Zhou, Guodong ^a  

^a SOOCHOW UNIVERSITY   (China)

^b Collaborative Innovation Center of Novel Software Technology and Industrialization ^*  (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL LINGUISTICS; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING SYSTEMS; SUPERVISED LEARNING; LINGUISTICS;

CROSS VALIDATION; METALEARNING; SEMI-SUPERVISED; SENTIMENT CLASSIFICATION; UNLABELED DATA; ENSEMBLE LEARNING;

LEARNING ALGORITHMS;

EID: 84944039610     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/p15-2005     Document Type: Conference Paper

References (19)

1. Blitzer J., M. Dredze and F. Pereira. 2007. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In Pro-ceedings ofACL-07, pp. 440-447
2. Blum A. and T. Mitchell. 1998. Combining Labeled and Unlabeled Data with Co-training. In Proceed-ings ofCOLT-98, pp. 92-100
3. Cui H., V. Mittal and M. Datar. 2006. Comparative Ex-periments on Sentiment Classification for Online Product Reviews. In Proceedings of AAAI-06, pp. 1265-1270
4. Dasgupta S. and V. Ng. 2009. Mine the Easy, Classify the Hard: A Semi-Supervised Approach to Auto-matic Sentiment Classification. In Proceedings of ACL-IJCNLP-09, pp. 701-709, 2009
5. Dzeroski S. and B. Zenko. 2004. Is Combining Classi-fiers with Stacking Better than Selecting the Best One? Machine Learning, vol.54(3), pp. 255-273, 2004
6. Gao W., S. Li, Y. Xue, M. Wang, and G. Zhou. 2014. Semi-supervised Sentiment Classification with Self-training on Feature Subspaces. In Proceedings ofCLSW-14, pp. 231-239
7. He Y., C. Lin and H. Alani. 2011. Automatically Ex-tracting Polarity-Bearing Topics for Cross-Domain Sentiment Classification. InProceedings ofACL-11, pp. 123-131
8. Li S., C. Huang, G. Zhou and S. Lee. 2010. Employing Personal/Impersonal Views in Supervised and Semi-supervised Sentiment Classification. In Pro-ceedings ofACL-10, pp. 414-423
9. Li S., R. Xia, C. Zong, and C. Huang. 2009. A Frame-work of Feature Selection Methods for Text Catego-rization. In Proceedings of ACL-IJCNLP-09, pp. 692-700
10. Li S., Y. Xue, Z. Wang, and G. Zhou. 2013. Active Learning for Cross-Domain Sentiment Classifica-tion.-Proceedings ofIJCAI-13, pp. 2127-2133
11. Li S., Z. Wang, G. Zhou and S. Lee. 2011. Semi-super-vised Learning for Imbalanced Sentiment Classifi-cation. In Proceedings of IJCAI-11, pp. 1826-1831
12. Liu B. 2012. Sentiment Analysis and Opinion Mining (Introduction and Survey). Morgan & Claypool Publishers, May 2012
13. Pang B. and L. Lee. 2008. Opinion Mining and Senti-ment Analysis: Foundations and Trends. Infor-mation Retrieval, vol.2(12), pp. 1-135
14. Pang B., L. Lee, and S. Vaithyanathan. 2002. Thumbs up? Sentiment Classification using Machine Learn-ing Techniques. In Proceedings of EMNLP-02, pp. 79-86
15. Riloff E., S. Patwardhan and J. Wiebe. 2006. Feature Subsumption for Opinion Analysis. In Proceedings ofEMNLP-06, pp. 440-448
16. Sindhwani V. and P. Melville. 2008. Document-Word Co-Regularization for Semi-supervised Sentiment Analysis. In Proceedings of ICDM-08, pp. 1025-1030
17. Wan X. 2009. Co-Training for Cross-Lingual Senti-ment Classification. In Proceedings of ACL-IJCNLP-09, pp. 235-243
18. Yang Y. and X. Liu. 1999. A Re-Examination of Text Categorization Methods. In Proceedings of SIGIR-99
19. Zhu X. and Z. Ghahramani. 2002. Learning from La-beled and Unlabeled Data with Label Propagation. CMU CALD Technical Report. CMU-CALD-02-107