Abstract
This work proposes a semi-sentiment classification method by exploiting co-occurrence opinion words. Our method is based on the observation that opinion words with similar sentiment have high possibility to co-occur with each other. We show co-occurrence opinion words are helpful for improving sentiment classification accuracy. We employ the co-training framework to conduct semi-supervised sentiment classification. Experimental results show that our proposed method has better performance than the Self-learning SVM method.
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References
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, pp. 92–100. ACM, New York (1998), http://doi.acm.org/10.1145/279943.279962
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995), http://dblp.uni-trier.de/db/journals/ml/ml20.html#CortesV95
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, EACL 1997, pp. 174–181. Association for Computational Linguistics, Stroudsburg (1997)
Lee, D., Jeong, O.R., Lee, S.G.: Opinion mining of customer feedback data on the web. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, ICUIMC 2008, pp. 230–235. ACM, New York (2008)
Li, S., Huang, C.-R., Zhou, G., Lee, S.Y.M.: 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 2010, pp. 414–423. Association for Computational Linguistics, Stroudsburg (2010)
Li, S.: Sentiment classification using subjective and objective views. International Journal of Computer Applications 80(7), 30–34 (2013)
Li, S., Guan, Z., Tang, L., Chen, Z.: Exploiting consumer reviews for product feature ranking. J. Comput. Sci. Technol. 27(3), 635–649 (2012)
Li, S., Hao, J.: Spectral clustering-based semi-supervised sentiment classification. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS, vol. 7713, pp. 271–283. Springer, Heidelberg (2012)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1-2), 1–135 (2008)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, EMNLP 2002, vol. 10, pp. 79–86. Association for Computational Linguistics, Stroudsburg (2002)
Turney, P.D.: 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 2002, pp. 417–424. Association for Computational Linguistics, Stroudsburg (2002)
Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inf. Syst. 21(4), 315–346 (2003)
Zhou, S., Chen, Q., Wang, X.: Active deep networks for semi-supervised sentiment classification. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING 2010, pp. 1515–1523. Association for Computational Linguistics, Stroudsburg (2010)
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Li, S., Hao, J., Jiang, Y., Jing, Q. (2013). Exploiting Co-occurrence Opinion Words for Semi-supervised Sentiment Classification. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_4
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DOI: https://doi.org/10.1007/978-3-642-53914-5_4
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