Integrating the optimal classifier set for sentiment analysis

Social Network Analysis and Mining

Volumn 5, Issue 1, 2015, Pages 1-13

  Lin, Yuming ^a   Wang, Xiaoling ^b   Li, You ^a   Zhou, Aoying ^b  

^a GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY   (China)

^b EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Approximation algorithm; Multiple classifiers; Sentiment analysis

Indexed keywords

APPROXIMATION ALGORITHMS; AUTOMATION; DATA MINING; INFORMATION RETRIEVAL SYSTEMS;

APPROXIMATE ALGORITHMS; AUTOMATIC IDENTIFICATION; CLASSIFICATION TASKS; COMBINATORIAL EXPLOSION; MULTIPLE CLASSIFIERS; OPTIMAL CLASSIFIERS; REAL-WORLD DATASETS; SENTIMENT ANALYSIS;

CLASSIFICATION (OF INFORMATION);

EID: 84947295912     PISSN: 18695450     EISSN: 18695469     Source Type: Journal    
DOI: 10.1007/s13278-015-0295-8     Document Type: Article

References (31)

1. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. In: Proceedings of the workshop on languages in social media. Association for Computational Linguistics, Stroudsburg, pp 30–38
2. Alm CO, Roth D, Sproat R (2005) Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, Stroudsburg, pp 579–586
3. Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification, Annual Meeting-Association For. Computational Linguistics 45(1):440–447
4. Borodin A, Lee HC, Ye Y (2012) Max-sum diversification, monotone submodular functions and dynamic updates. In: Proceedings of the 31st symposium on principles of database systems. ACM, New York, pp 155–166
5. Breiman L (1996) Bagging predictors. Mach Learn. 24(2), (Springer-Verlag, New York), pp 123–140
6. Breiman L (1996) Random forest. Machine Learning. 45(1), (Springer-Verlag, New York), pp 5–32
7. Cui H, Mittal V, Datar M (2006) Comparative experiments on sentiment classification for online product reviews, AAAI, vol 6. AAAI Press, Palo Alto, pp 1265–1270
8. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci. 55(1), (Elsevier, Oxford), pp 119–139
9. Gamon M, Aue A (2005) Automatic identification of sentiment vocabulary: exploiting low association with known sentiment terms. In: Proceedings of the ACL workshop on feature engineering for machine learning in natural language processing. Association for Computational Linguistics, Stroudsburg, pp 57–64
10. Hatzivassiloglou V, McKeown KR (1997) Predicting the semantic orientation of adjectives. In: Proceedings of the 35th annual meeting of the association for computational linguistics and eighth Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, pp 174–181
11. Hu X, Tang J, Gao H, Liu H (2013) Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd international conference on World Wide Web. ACM, New York, pp 607–618
12. Kamps J, Marx M, Mokken RJ, Rijke MD (2004) Using wordnet to measure semantic orientations of adjectives. In: Proceedings of the 4th LREC, pp. 1115–1118
13. Kim S, Hovy E (2006) Identifying and analyzing judgment opinions. In: Proceedings of the main conference on human language technology conference of the North American chapter of the association of computational linguistics. Association for Computational Linguistics, Stroudsburg, pp 200C207
14. Lin H, Bilmes J (2011) A class of submodular functions for document summarization. In: ACL Association for Computational Linguistics, Stroudsburg, pp 510–520
15. Lin Y, Wang X, Zhang J, Zhou A (1990) Introduction to wordnet: an on-line lexical database. Int J Lexicogr, 3(4). (Oxford Univ Press, Oxford), pp 235–244
16. Lin Y, Zhang J, Wang X, Zhou A (2012) An information theoretic approach to sentiment polarity classification. In: Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality. ACM, New York, pp 35–40
17. Lin Y, Zhang J, Wang X, Zhou A (2012) Sentiment classification via integrating multiple feature presentations. In: Proceedings of the 21st international conference companion on World Wide Web. ACM, New York, pp 569–570
18. Liu B (2010) Sentiment analysis and subjectivity, Handbook of natural language processing, vol 2. (Chapman & Hall, London), pp 627–666
19. Martineau J, Finin T, Joshi A, Patel S (2009) Improving binary classification on text problems using differential word features. In: Proceedings of the 18th ACM conference on Information and knowledge management. ACM, New York, pp 2019–2024
20. Matsumoto S, Takamura H, Okumura M (2005) Sentiment classification using word sub-sequences and dependency sub-trees. Advances in knowledge discovery and data mining. Springer, New York, pp 301–311
21. Miller GA, Beckwith R, Fellbaum C, Gross D, Miller KJ (1990) Introduction to wordnet: An on-line lexical database. Int J Lexicogr, 3(4). (Oxford Univ Press, Oxford), pp 235–244
22. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10. Association for Computational Linguistics, Stroudsburg, pp 79–86
23. Ravi SS, Rosenkrantz DJ, Rayi GK (1994) Heuristic and special case algorithms for dispersion problems. Oper Res 42(2), (INFORMS), pp 299–310
24. Skalak DB (1996) The sources of increased accuracy for two proposed boosting algorithms. In: Proceedings of American Association for Artificial Intelligence, AAAI-96, Integrating Multiple Learned Models Workshop. (Citeseer), pp 1133–1138
25. Tang J, Nobata C, Dong A, Chang Y, Liu H (2015) Propagation-based sentiment analysis for microblogging data. In: SIAM international conference on data mining
26. Turney PD (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. Association for Computational Linguistics, Stroudsburg, pp 417–424
27. Vee E, Srivastava U, Shanmugasundaram J, Bhar P, Yahia SA (2008) Efficient computation of diverse query results. In: IEEE 24th International Conference on Data Engineering, IEEE, Washinton, pp 228–236
28. Wolpert DH (1992) Stacked generalization. Neural Networks. 5(2), (Elsevier, Oxford), pp 241–260
29. Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification. Inf Sci. 181(6), (Elsevier, Oxford), pp 1138–1152
30. Xu L, Li B, Chen E (2012) Ensemble pruning via constrained Eigen-Optimization. In: 12th international conference on data mining. IEEE, Washington, pp 715–724
31. Zhou Z, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell, 137(1). (Elsevier, Oxford), pp 239–263