One-Class Support Vector Machine for Sentiment Analysis of Movie Review Documents
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:71570", author = "Chothmal and Basant Agarwal", title = "One-Class Support Vector Machine for Sentiment Analysis of Movie Review Documents", 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.", volume = "9", number = "12", pages = "2458-4", }
