Ensemble of feature sets and classification algorithms for sentiment classification

Information Sciences

Volumn 181, Issue 6, 2011, Pages 1138-1152

  Xia, Rui ^a   Zong, Chengqing ^a   Li, Shoushan ^b  

^a INSTITUTE OF AUTOMATION   (China)

^b SOOCHOW UNIVERSITY   (China)

Author keywords

Classifier combination; Comparative study; Ensemble learning; Sentiment classification; Text classification

Indexed keywords

CLASSIFIER COMBINATION; COMPARATIVE STUDIES; ENSEMBLE LEARNING; SENTIMENT CLASSIFICATION; TEXT CLASSIFICATION;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CLASSIFIERS;

TEXT PROCESSING;

EID: 79251598720     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.11.023     Document Type: Article

References (41)

1. F. Benamara, C. Cesarano, A. Picariello, D. Reforgiato, V.S. Subrahmanian, Sentiment analysis: adjectives and adverbs are better than adjectives alone, in: Proceedings of the International Conference on Weblogs and Social Media (ICWSM), 2007.
2. J. Blitzer, M. Dredze, F. Pereira, Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification, in: Proceedings of the Association for Computational Linguistics (ACL), 2007.
3. J. Bridle Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition Neurocomputing: Algorithms, Architectures and Applications 227 1990 236
4. H. Cui, V. Mittal, M. Datar, Comparative experiments on sentiment classification for online product reviews, in: Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI), 2006.
5. K. Dave, S. Lawrence, D.M. Pennock, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, in: Proceedings of the International World Wide Web Conference (WWW), 2003, pp. 519-528.
6. Y.-S. Dong, K.-S. Han, A comparison of several ensemble methods for text categorization, in: The 2004 IEEE International Conference on Services Computing (SCC), 2004, pp. 419-422.
7. S. Džeroski, and B. Ženko Is combining classifiers with stacking better than selecting the best one? Machine Learning 54 2004 255 273
8. Y. Freund, and R. Schapire Large margin classification using the perceptron algorithm Machine Learning 37 1999 277 296
9. A. Fujino, N. Ueda, and K. Saito A hybrid generative/discriminative approach to text classification with additional information Information Processing and Management 43 2007 379 392
10. M. Gamon, Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis, in: Proceedings of the International Conference on Computational Linguistics (COLING), 2004.
11. S. Hashem Optimal linear combinations of neural networks Neural Networks 10 1997 599 614
12. V. Hatzivassiloglou, J. Wiebe, Effects of adjective orientation and gradability on sentence subjectivity, in: Proceedings of the International Conference on Computational Linguistics (COLING), 2000, pp. 299-305.
13. T. Ho, J. Hull, and S. Srihari Decision combination in multiple classifier systems IEEE Transactions on Pattern Analysis and Machine Intelligence 16 1994 66 75
14. T. Joachims, C. Nedellec, and C. Rouveirol Text categorization with support vector machines: learning with many relevant Proceedings of the European Conference on Machine Learning 1998 Springer 137 142
15. M. Joshi, C. Penstein-Rosé, Generalizing dependency features for opinion mining, in: Proceedings of the 47th ACL and the 4th IJCNLP Conference, Association for Computational Linguistics, 2009, pp. 313-316.
16. B. Juang, and S. Katagiri Discriminative learning for minimum error classification [patternrecognition] IEEE Transactions on Signal Processing 40 1992 3043 3054
17. J. Kittler Combining classifiers: a theoretical framework Pattern Analysis and Applications 1 1998 18 27
18. L. Larkey, and W. Croft Combining classifiers in text categorization Proceeding of ACM SIGIR Conference 1996 ACM New York, NY, USA 289 297
19. D. Lewis Naive (Bayes) at forty: the independence assumption in information retrieval Lecture Note in Computer Science 1398 1998 4 18
20. S. Li, C. Zong, X. Wang, Sentiment classification through combining classifiers with multiple feature sets, in: Proceedings of the IEEE International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE 07), 2007, pp. 135-140.
21. B. Liu Sentiment analysis: a multifaceted problem IEEE Intelligent System 25 2010 76 80
22. C. Liu Classifier combination based on confidence transformation Pattern Recognition 38 2005 11 28
23. W. Liu, X. Quan, Feng Min, and Q. Bite A short text modeling method combining semantic and statistical information Information Sciences 180 2010 4031 4041
24. A. McCallum, K. Nigam, A comparison of event models for naive Bayes text classification, in: Proceedings of the AAAI Workshop on Learning for Text Categorization, Citeseer, 1998.
25. R. McDonald, F. Pereira, K. Ribarov, J. Hajic, Non-projective dependency parsing using spanning tree algorithms, in: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), 2005, pp. 523-530.
26. A. Ng, and M. Jordan On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes Advances in Neural Information Processing Systems 2 2002 841 848
27. K. Nigam, J. Lafferty, A. McCallum, Using maximum entropy for text classification, in: Proceedings of IJCAI Workshop on Machine Learning for Information Filtering, 1999, pp. 61-67.
28. B. Pang, L. Lee, A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts, in: Proceedings of the Association for Computational Linguistics (ACL), 2004, pp. 271-278.
29. B. Pang, and L. Lee Opinion mining and sentiment analysis Foundations and Trends in Information Retrieval 2 2008 1 135
30. B. Pang, L. Lee, S. Vaithyanathan, Thumbs up? Sentiment classification using machine learning techniques, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2002, pp. 79-86.
31. J. Platt Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods Advances in Large Margin Classifiers 1999
32. R. Raina, Y. Shen, A. Ng, and A. McCallum Classification with hybrid generative/discriminative models Advances in Neural Information Processing Systems 16 2004 545 552
33. A. Ratnaparkhi, A maximum entropy model for part-of-speech tagging, in: Proceedings of the Empirical Methods in Natural Language Processing Conference (EMNLP), 1996, pp. 133-142.
34. E. Riloff, S. Patwardhan, J. Wiebe, Feature subsumption for opinion analysis, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2006, pp. 440-448.
35. E. Riloff, J. Wiebe, T. Wilson, Learning subjective nouns using extraction pattern bootstrapping, in: Proceedings of the Conference on Natural Language Learning (CoNLL), 2003, pp. 25-32.
36. V. Subrahmanian, and D. Reforgiato AVA: adjective-verb-adverb combinations for sentiment analysis IEEE Intelligent Systems 23 2008 43 50
37. C. Sutton, A. McCallum, An introduction to conditional random fields for relational learning, in: Introduction to Statistical Relational Learning, 2007.
38. M. Whitehead, and L. Yaeger Sentiment mining using ensemble classification models International Conference on Systems, Computing Sciences and Software Engineering (SCSS 08) 2008 Springer
39. Y. Yang, and X. Liu A re-examination of text categorization methods Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 1999 ACM New York, NY, USA 42 49
40. H.-T. Zheng, B.-Y. Kang, and H.-G. Kim Exploiting noun phrases and semantic relationships for text document clustering Information Sciences 179 2009 2249 2262
41. R. Xia, C. Zong, Exploring the use of word relation features for sentiment classification, in: Proceedings of the 23rd International Conference on Computational Linguistics (COLING), 2010, pp. 1336-1344.