Abstract
Traditional sentiment feature extraction methods in document-level sentiment classification either count the frequencies of sentiment words as features, or the frequencies of modified and unmodified instances of each of these words. However, these methods do not represent the sentiment words’ linguistic context efficiently. We propose a novel method and feature set to handle the contextual polarity of sentiment words efficiently. Our experiments on both movie and product reviews show a significant improvement in the classifier’s performance (an overall accuracy increase of 2%), in addition to statistical significance of our feature set over the traditional feature set. Also, compared with other widely-used feature sets, most of our features are among the key features for sentiment classification.
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References
Abbasi, A., Chen, H., Salem, A.: Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums. ACM Trans. Inf. Syst. 26, 1–34 (2008)
Morsy, S.: Recognizing contextual valence shifters in document-level sentiment classification. Masters Thesis, The American University in Cairo (2011)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: Proc. Assoc. Computational Linguistics, pp. 440–447 (2007)
Carrillo de Albornoz, J., Plaza, L., Gervás, P.: A hybrid approach to emotional sentence polarity and intensity classification. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL 2010), pp. 153–161 (2010)
Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22, 110–125 (2006)
Dang, Y., Zhang, Y., Chen, H.: A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intelligent Systems 25, 46–53 (2010)
Polanyi, L., Zaenen, A.: Contextual valence shifters. Computing Attitude and Affect in Text: Theory and Applications 20, 1–10 (2006)
Whitelaw, C., Garg, N., Argamon, S.: Using appraisal groups for sentiment analysis. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM 2005), pp. 625–631 (2005)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT 2005), vol. 22, pp. 347–354 (2005)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Computational Linguistics 37, 267–307 (2011)
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© 2012 Springer-Verlag Berlin Heidelberg
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Morsy, S.A., Rafea, A. (2012). Improving Document-Level Sentiment Classification Using Contextual Valence Shifters. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds) Natural Language Processing and Information Systems. NLDB 2012. Lecture Notes in Computer Science, vol 7337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31178-9_30
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DOI: https://doi.org/10.1007/978-3-642-31178-9_30
Publisher Name: Springer, Berlin, Heidelberg
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