Reserved Self-training: A Semi-supervised Sentiment Classification Method for Chinese Microblogs

6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Proceedings of the Main Conference

Volumn , Issue , 2013, Pages 455-462

  Liu, Zhiguang ^a   Dong, Xishuang ^a   Guan, Yi ^a   Yang, Jinfeng ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFIERS; CLASSIFICATION METHODS; COMPUTING PROCESS; CONFIDENCE SCORE; MICRO-BLOG; PERFORMANCE; SELF-TRAINING; SEMI-SUPERVISED; SEMI-SUPERVISED METHOD; SENTIMENT CLASSIFICATION;

SUPERVISED LEARNING;

EID: 85121739761     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Maria-Florina Balcan, Avrim Blum, and Ke Yang. 2004. Co-training and expansion: Towards bridging theory and practice. In Advances in neural information processing systems, pages 89–96.
2. Soumya Banerjee and Nitin Agarwal. 2012. Analyzing collective behavior from blogs using swarm intelligence. Knowledge and information systems, 33(3):523–547.
3. Ricardo Barandela, José Salvador Sánchez, Vicente Garcıa, and Edgar Rangel. 2003. Strategies for learning in class imbalance problems. Pattern Recognition, 36(3):849–851.
4. Gustavo EAPA Batista, Ronaldo C Prati, and Maria Carolina Monard. 2004. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1):20–29.
5. Avrim Blum and Tom Mitchell. 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the eleventh annual conference on Computational learning theory, pages 92–100. ACM.
6. Ming-Wei Chang, Lev Ratinov, and Dan Roth. 2007. Guiding semi-supervision with constraint-driven learning. Urbana, 51:61801.
7. Olivier Chapelle, Bernhard Schölkopf, Alexander Zien, et al. 2006. Semi-supervised learning, volume 2. MIT press Cambridge.
8. Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002. Smote: synthetic minority over-sampling technique. J. Artif. Int. Res., 16(1):321–357, June.
9. Nitesh V Chawla, Nathalie Japkowicz, and Aleksander Kotcz. 2004. Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter, 6(1):1–6.
10. Chris Drummond, Robert C Holte, et al. 2003. C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In Workshop on Learning from Imbalanced Datasets II, volume 11. Citeseer.
11. Yen-Hsien Lee, Paul Jen-Hwa Hu, Tsang-Hsiang Cheng, and Ya-Fang Hsieh. 2012. A cost-sensitive technique for positive-example learning supporting content-based product recommendations in b-to-c e-commerce. Decision Support Systems, 53(1):245–256.
12. Shoushan Li, Zhongqing Wang, Guodong Zhou, and Sophia Yat Mei Lee. 2011. Semi-supervised learning for imbalanced sentiment classification. In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Three, pages 1826–1831. AAAI Press.
13. Charles X Ling, Qiang Yang, Jianning Wang, and Shichao Zhang. 2004. Decision trees with minimal costs. In Proceedings of the twenty-first international conference on Machine learning, page 69. ACM.
14. Bing Liu, Minqing Hu, and Junsheng Cheng. 2005. Opinion observer: analyzing and comparing opinions on the web. In Proceedings of the 14th international conference on World Wide Web, pages 342–351. ACM.
15. Jonathan Ortigosa-Hernández, Juan Diego Rodríguez, Leandro Alzate, Manuel Lucania, Iñaki Inza, and Jose A Lozano. 2012. Approaching sentiment analysis by using semi-supervised learning of multidimensional classifiers. Neurocomputing, 92:98–115.
16. Bo Pang and Lillian Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting on Association for Computational Linguistics, page 271. Association for Computational Linguistics.
17. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pages 79–86. Association for Computational Linguistics.
18. Ellen Riloff, Janyce Wiebe, and Theresa Wilson. 2003. Learning subjective nouns using extraction pattern bootstrapping. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4, pages 25–32. Association for Computational Linguistics.
19. Russell J Ryan. 2011. Groundtruth budgeting: a novel approach to semi-supervised relation extraction in medical language. Ph.D. thesis, Massachusetts Institute of Technology.
20. Aarti Singh, Robert Nowak, and Xiaojin Zhu. 2008. Unlabeled data: Now it helps, now it doesn’t. In Advances in Neural Information Processing Systems, pages 1513–1520.
21. Peter D Turney. 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics, pages 417–424. Association for Computational Linguistics.
22. Gary M Weiss. 2004. Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter, 6(1):7–19.
23. Wei Wu and Mari Ostendorf. 2013. Graph-based query strategies for active learning.
24. Show-Jane Yen and Yue-Shi Lee. 2009. Cluster-based under-sampling approaches for imbalanced data distributions. Expert Systems with Applications, 36(3):5718–5727.
25. Bianca Zadrozny, John Langford, and Naoki Abe. 2003. Cost-sensitive learning by cost-proportionate example weighting. In Data Mining, 2003. ICD-M 2003. Third IEEE International Conference on, pages 435–442. IEEE.