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Sentimental Analysis using Asymmetric Least Squares Twin Support Vector Machine (LSTSVM)


Affiliations
1 Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, India
2 Department of Computer Application, Sree Saraswathi Thyagaraja College, Pollachi, India
     

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Sentiment analysis known as opinion mining is the process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions and emotions expressed within an online mention.  It is also known as opinion mining used in the voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. This paper works on finding approaches that generate output with good accuracy through (aLSTSVM) Asymmetric Least Squares Twin Support Vector Machine. aLSTSVM is a new version of support vector machine (SVM) based on asymmetric least square and non-parallel twin hyperplanes. It is an efficient fast algorithm for binary classification and its parameters depend on the nature of the problem. A result on several benchmark datasets is applied to train a sentiment classifier in order to demonstrate the accuracy of the proposed algorithm. N-grams and different weighting scheme were used to take out the most classical features. It also analyzes Chi-Square weight features to select informative features for the classification. Experimental analysis reveals that by using Chi-Square feature selection in aLSTSVM may provide significant improvement on classification accuracy.


Keywords

Chi-Square Weight, Asymmetric Least Squares Support Vector Machine (aLSTSVM), Support Vector Machine (SVM).
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  • Feldman, R., 2013. Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), pp.82-89.

  • Mostafa, M.M., 2013. More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), pp.4241-4251.

  • Pang, B. and Lee, L., 2004, July. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting on Association for Computational Linguistics (p. 271). Association for Computational Linguistics.

  • Taboada, M., Anthony, C. and Volt, K., 2006, May. Methods for creating semantic orientation dictionaries. In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC’06) (pp. 427-432).

  • C. Corinne and V. Vapid, “Support-vector networks”, Machine learning, vol. 20, no. 3, (1995), pp. 273-297.

  • Sale, M.R., Martin-Valdivia, M.T., Montejo-Ráez, A. and Ureña-López, L.A., 2011. Experiments with SVM to classify opinions in different domains. Expert Systems with Applications, 38(12), pp.14799-14804.

  • Uysal, A.K. and Gunal, S., 2012. A novel probabilistic feature selection method for text classification. Knowledge-Based Systems, 36, pp.226-235.

  • MartinezCamera, E., Valdivia, M., Teresa, M., Peres Ortega, J.M. and Urea Lopez, L.A., 2011. Technical de classification de opinions palisades a un corpus en Espanola.

  • Liu, B., 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), pp.1-167.

  • Pang, B., Lee, L. and Vaithyanathan, S., 2002, July. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Association for Computational Linguistics.

  • Miller, G.A., 1995. Word Net: a lexical database for English. Communications of the ACM, 38(11), pp.39-41.

  • Treaty, A., Agrawal, A. and Rath, S.K., 2015.Classification of Sentimental Reviews Using Machine Learning Techniques. Procedia Computer Science, 57, pp.821-829.

  • S. Gunal and R. Edizkan, “Subspace based feature selection for pattern recognition,” Information Sciences, vol. 178, no. 19, pp. 3716–3726, 2008.

  • T. L. Ladha, “Feature selection methods and algorithms,” International Journal on Computer Science and Engineering (IJCSE), vol. 3, p. 5, 2011.

  • K. Uysal and S. Gunal, “The impact of preprocessing on text classification,” Information Processing & Management, vol. 50, no. 1, pp. 104 – 112, 2014.

  • Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.

  • T. Joachims, “Text categorization with support vector machines: Learning with many relevant features,” in Machine Learning: ECML-98, ser. Lecture Notes in Computer Science, C. Nedellec´ and C. Rouveirol, Eds. Springer Berlin Heidelberg, 1998, vol. 1398, pp. 137–142.

  • Martinez Camara, V. Martin, M. Teresa, J. M. Perea Ortega, and L. A. Urena Lopez, “Tecnicas de clasificacion de opinions aplicadas a un corpus en espanol,” Procesamiento de Language Natural, vol. 47, pp. 163–170, 2011.

  • Sebastiani, “Machine learning in automated text categorization,” ACM Comput. Surv., vol. 34, no. 1, pp. 1–47, Mar. 2002.

  • Mangasarian O. L. and Edward W. W., “Multi-surface proximal support vector classification via generalized eigenvalues”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, (2006), pp.69–74.

  • J. R. Khemchandani and S. Chandra, “Twin support vector machine for pattern classification”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 29, no. 5, (2007), pp. 905–910.

  • T. Divya and S. Agarwal, “Twin Support Vector Machine: A review from 2007 to 2014”, Egyptian Informatics Journal, vol. 16, no. 1, (2015), pp. 55-69.

  • T. Divya and S. Agarwal, “Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes”, Advances in Artificial Neural Systems, vol. 2015, (2015).

  • X. Yitian, R. Guo and L. Wang, “A twin multi-class classification support vector machine”, Cognitive Computation, vol. 5, no. 4, (2013), pp.580-588.


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  • Sentimental Analysis using Asymmetric Least Squares Twin Support Vector Machine (LSTSVM)

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Authors

N. Saranya
Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, India
R. Gunavathi
Department of Computer Application, Sree Saraswathi Thyagaraja College, Pollachi, India

Abstract


Sentiment analysis known as opinion mining is the process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions and emotions expressed within an online mention.  It is also known as opinion mining used in the voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. This paper works on finding approaches that generate output with good accuracy through (aLSTSVM) Asymmetric Least Squares Twin Support Vector Machine. aLSTSVM is a new version of support vector machine (SVM) based on asymmetric least square and non-parallel twin hyperplanes. It is an efficient fast algorithm for binary classification and its parameters depend on the nature of the problem. A result on several benchmark datasets is applied to train a sentiment classifier in order to demonstrate the accuracy of the proposed algorithm. N-grams and different weighting scheme were used to take out the most classical features. It also analyzes Chi-Square weight features to select informative features for the classification. Experimental analysis reveals that by using Chi-Square feature selection in aLSTSVM may provide significant improvement on classification accuracy.


Keywords


Chi-Square Weight, Asymmetric Least Squares Support Vector Machine (aLSTSVM), Support Vector Machine (SVM).

References