One-Class Support Vector Machine for Sentiment Analysis of Movie Review Documents

Authors: Chothmal, Basant Agarwal

Abstract:

Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model.

Keywords: Feature selection methods, Machine learning, NB, One-class SVM, Sentiment Analysis, Support Vector Machine.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110393

References:

[1] Liu B., “Sentiment Analysis and Opinion Mining”, Synthesis Lectures on Human Language Technologies, Morgan & Claypool Publishers, 2012.
[2] Liu B., “Sentiment Analysis and Subjectivi,” Handbook of Natural Language Processing”, 2nd ed., N. Indurkhya and F.J. Damerau, eds., Chapman & Hall / CRC Press, 2010, pp. 627-666.
[3] Agarwal B., Mittal N., “Enhancing Performance of Sentiment Analysis by Semantic Clustering of Features”, In IETE Journal of Research, Taylor and Francis, 2014, pp: 1-9.
[4] Agarwal B., Mittal N., “Prominent Feature Extraction for Sentiment Analysis”, Springer Book Series: Socio-Affective Computing series, ISBN: 978-3-319-25343-5, DOI: 10.1007/978-3-319-25343-5, pages: 1- 115.
[5] 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), 2002, pp: 79-86.
[6] Tan S., Zhang J., “An empirical study of sentiment analysis for Chinese documents”, In Expert Systems with Applications, Vol: 34, No: 4, 2008, pp: 2622-2629.
[7] O’keefe T., Koprinska I., “Feature Selection and Weighting Methods in Sentiment Analysis”, In Proceedings of the 14th Australasian Document Computing Symposium, Sydney, Australia, 2009, pp: 67-74.
[8] Ye Q., Zhang Z., Law R., “Sentiment classification of online reviews to travel destinations by supervised machine learning approaches”, In Expert Systems with Applications, Vol: 36, No: 3, 2009, pp: 6527-6535.
[9] Cui H., Mittal V., Datar M., ”Comparative experiments on sentiment classification for online product reviews”, In Proceedings of the 21st national conference on Artificial Intelligence, 2006, pp: 1265-1270.
[10] Moraes R., Valiati JF, Neto WPG, “Document-level sentiment classification: An empirical comparison between SVM and ANN”, In Expert Systems with Applications, Vol: 40, No: 2, 2013, pp: 621-633.
[11] Saleh MR, Martin-Valdivia MT, Montejo-Raez A., Urena-Lopez LA, “Experiments with SVM to classify opinions in different domains”, In Expert Systems with Applications, Vol: 38, No: 12, 2011, pp: 14799- 14804.
[12] Li S., Zong C., Wang X., “Sentiment Classification through Combining classifiers with multiple feature sets”, In Proceedings of the International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE), 2007, pp: 135- 140.
[13] Tsutsumi K., Shimada K., Endo T., “Movie Review Classification Based on a Multiple Classifier”, In Proceedings of the Annual meetings of the Pacific Asia Conference on Language, Information and Computation (PACLIC), 2007, pp: 481-488.
[14] Xia R., Zong C., Li S., “Ensemble of Feature Sets and Classification Algorithms for Sentiment Classification”. In Journal of Information Sciences, Vol: 181, No: 6, 2011, pages: 1138-1152.
[15] Prabowo R., Thelwall M., “Sentiment analysis: A combined approach”, In Journal of Informatics, Vol: 3, No: 2, 2009, pp:143-157.
[16] Agarwal B., Mittal N., “Optimal Feature Selection for Sentiment Analysis”, In 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2013),Vol-7817, pages-13-24, Greece, Samos. 2013.
[17] Schaolkopf B., Platt J. C., Shawe-Taylor J. C., Smola A. J., Williamson, R.C, “Estimating the support of a high-dimensional distribution.”, In Neural Comput.13, 7, 1443-1471.
[18] 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), 2004, pp. 271- 278.
[19] Agarwal B., Mittal N., Bansal P., Garg S., “Sentiment Analysis Using Common-Sense and Context Information”, In Computational Intelligence and Neuroscience, Article ID 715730, 9 pages, 2015, DOI: http://dx.doi.org/10.1155/2015/715730.
[20] Agarwal B., Mittal N., “Prominent Feature Extraction for Review Analysis: An Empirical Study”, In Journal of Experimental and theoretical Artificial Intelligence, Taylor Francis, 2014, DOI:10.1080/0952813X.2014.977830.