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Sentiment Classification using Subjective and Objective Views
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 80 - Number 7 |
| Year of Publication: 2013 |
| Authors: Suke Li |
10.5120/13875-1749
|
Suke Li . Sentiment Classification using Subjective and Objective Views. International Journal of Computer Applications. 80, 7 ( October 2013), 30-34. DOI=10.5120/13875-1749
Abstract
This work proposes a new semi-supervised sentiment classification method by exploiting a large number of unlabeled instances to conduct sentiment classification for Web consumer reviews. In the proposed method every consumer review has two views: subjective view and objective view. The subjective view of a consumer review reflects the opinions expressed by opinion words, while the objective view is constructed by the remaining text features. This work is trying to combine two kinds of views to carry out sentiment classification. The method is based on the co-training framework which needs three basic sentiment classifiers to iteratively get the final sentiment classifier. In the proposed method, the first sentiment classifier is constructed using the common unigram features coming from consumer reviews. The second sentiment classifier is trained on the subjective views constructed by opinion words extracted from consumer reviews. The remaining text features of these reviews are used for obtaining the objective views which can be trained for the third classifier. Experimental results show the proposed method is effective, and it has better performance than the Self-learning SVM method.
References
- A. Blum and T. Mitchell. "Combining labeled and unlabeled data with co-training", In Proceedings of the eleventh Annual Conference on Computational Learning Theory, COLT'98, pp. 92–100, 1998.
- V. Hatzivassiloglou and K. R. McKeown. "Predicting the semantic orientation of adjectives", In Proceedings of the eighth Conference on European chapter of the Association for Computational Linguistics, EACL '97, pp. 174–181, 1997.
- P. D. Turney and M. L. Littman, "Measuring praise and criticism: Inference of semantic orientation from association", ACM Trans. Inf. Syst. , Vol. 21, No. 4, pp. 315–346, 2003.
- P. D. Turney, "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, ACL '02, pp. 417–424, 2002.
- B. Pang, L. Lee, and S. Vaithyanathan. "Thumbs up?: sentiment classification using machine learning techniques", In Proceedings of the ACL-02 Conference on Empirical methods in Natural Language Processing, Vol. 10, EMNLP '02, pp. 79–86, 2002.
- B. Pang and L. Lee. "Opinion mining and sentiment analysis", Found. Trends Inf. Retr. , Vol. No. (1-2), pp. 1–135, 2008.
- R. Moraes, J. F. Valiati, and W. P. G Neto. "Document-level sentiment classification: An empirical comparison between SVM and ANN", Expert Syst. Appl. , Vol. 40, No. 2, pp. 621–633, 2013.
- R. Xia, C. Zong, and S. Li. "Ensemble of feature sets and classification algorithms for sentiment classification", Inf. Sci. , Vol. 181, No. 6, pp. 1138–1152, 2011.
- A J. Blitzer, R. McDonald, and F. Pereira. "Domain adaptation with structural correspondence learning", In Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 120-128, 2006.
- S. J. Pan, X. Ni, J. Sun, Q. Yang, and Z. Chen. "Cross-domain sentiment classification via spectral feature alignment", In Proceedings of the 19th International Conference on World Wide Web, WWW '10, pp. 751–760, 2010.
- D. Bollegala, D. Weir, and J. Carroll. "Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification", In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 132–141, 2011.
- S. Gao and H. Li. "A cross-domain adaptation method for sentiment classification using probabilistic latent analysis", In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM'11, pp. 1047–1052, 2011.
- L. Qiu, W. Zhang, C. Hu, and K. Zhao. "Selc: a self supervised model for sentiment classification", In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM '09, pp. 929–936, 2009.
- K. Sarvabhotla, P. Pingali, and V. Varma. "Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents", Inf. Retr. , Vol. 14, No. 3, pp. 337–353, 2011.
- S. Zhou, Q. Chen, and X. Wang. "Active deep networks for semi-supervised sentiment classification", In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING '10, pp. 1515–1523, 2010.
- S. Li, C. Huang, G. Zhou, and S. Y. M. Lee. "Employing personal/impersonal views in supervised and semi-supervised sentiment classification", In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL '10, pp. 414–423.
- S. Li and J. Hao. "Spectral clustering-based semi-supervised sentiment classification", In Proceeding of 8th International Conference of Advanced Data Mining and Applications, pp. 271–283, 2012.
- S. Li, Z. Guan, L. Tang, and Z. Chen. "Exploiting consumer reviews for product feature ranking", J. Comput. Sci. Technol. , Vol. 27, No. 3, pp. 635–649, 2012.
- C. Cortes and V. Vapnik, "Support-vector networks", Machine Learning, Vol. 20, No. 3, pp. 273–297, 1995.
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