Abstract
Sentiment Analysis (SA) research has increased tremendously in recent times. Sentiment analysis deals with the methods that automatically process the text contents and extract the opinion of the users. In this paper, unigram and bi-grams are extracted from the text, and composite features are created using them. Part of Speech (POS) based features adjectives and adverbs are also extracted. Information Gain (IG) and Minimum Redundancy Maximum Relevancy (mRMR) feature selection methods are used to extract prominent features. Further, effect of various feature sets for sentiment classification is investigated using machine learning methods. Effects of different categories of features are investigated on four standard datasets i.e. Movie review, product (book, DVD and electronics) review dataset. Experimental results show that composite features created from prominent features of unigram and bi-gram perform better than other features for sentiment classification. mRMR is better feature selection method as compared to IG for sentiment classification. Boolean Multinomial Naïve Bayes (BMNB) algorithm performs better than Support Vector Machine (SVM) classifier for sentiment analysis in terms of accuracy and execution time.
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References
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)
Agarwal, B., Mittal, N.: Categorical Probability Proportion Difference (CPPD): A Feature Selection Method for Sentiment Classification. In: Proceedings of the 2nd Workshop on Sentiment Analysis where AI Meets Psychology (SAAIP 2012), COLING 2012, Mumbai, pp. 17–26 (2012)
Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the Association for Computational Linguistics (ACL), pp. 271–278 (2004)
Mullen, T., Collier, N.: Sentiment analysis using support vector machines with diverse information sources. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 412–418 (2004)
Verma, S., Bhattacharyya, P.: Incorporating semantic knowledge for sentiment analysis. In: Proceedings of ICON 2009, Hyderabad, India (2009)
Turney, P.: Thumbs up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: ACL 2002, pp. 417–424 (2002)
Porter, M.F.: An Algorithm for Suffix Stripping. Program 14(3), 130–137 (1980)
Liu, B.: Sentiment Analysis and Subjectivity. In: Indurkhya, N., Damerau, F.J. (eds.) Handbook of Natural Language Processing, 2nd edn., pp. 627–666. Chapman & Hall (2010)
Abbasi, A., France, S., Zhang, Z., Chen, H.: Selecting Attributes for Sentiment Classification Using Feature Relation Networks. IEEE Transactions on Knowledge and Data Engineering 23, 447–462 (2011)
Witten, I.H., Frank, E.: Data mining: Practical Machine Learning Tools and techniques. Morgan Kaufmann, San Francisco (2005)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)
Tan, S., Zhang, J.: An empirical study of sentiment analysis for chinese documents. Expert Systems with Applications 34, 2622–2629 (2008)
Wang, S., Li, D., Wei, Y., Li, H.: A Feature Selection Method Based on Fisher’s Discriminant Ratio for Text Sentiment Classification. In: Liu, W., Luo, X., Wang, F.L., Lei, J. (eds.) WISM 2009. LNCS, vol. 5854, pp. 88–97. Springer, Heidelberg (2009)
O’Keefe, T., Koprinska, I.: Feature Selection and Weighting Methods in Sentiment Analysis. In: Proceedings of the 14th Australasian Document Computing Symposium (2009)
Nicholls, C., Song, F.: Comparison of feature selection methods for sentiment analysis. In: Farzindar, A., Kešelj, V. (eds.) Canadian AI 2010. LNCS, vol. 6085, pp. 286–289. Springer, Heidelberg (2010)
Abbasi, A., Chen, H.C., Salem, A.: Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Transactions on Information Systems 26(3), Article no:12 (2008)
Manning, C.D., Raghvan, P., Schutze, H.: Introduction to information retrieval. Cambridge University Press, Cambridge (2008)
WEKA. Open Source Machine Learning Software Weka, http://www.cs.waikato.ac.nz/ml/weka/
Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., Subrahmanian, V.S.: Sentiment analysis: Adjectives and adverbs are better than adjectives alone. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM) (2007)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI 1995 Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1137–1143 (1995)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: Proceedings of the Association for Computational Linguistics (ACL), pp. 440–447 (2007)
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Agarwal, B., Mittal, N. (2013). Optimal Feature Selection for Sentiment Analysis. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37256-8_2
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DOI: https://doi.org/10.1007/978-3-642-37256-8_2
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