A Review of Feature Selection Techniques for Opinion Mining Systems

S. Jha¹ , S. Gupta² , Y. Sharma³

Section:Research Paper, Product Type: Journal-Paper
Vol.6 , Issue.9 , pp.65-69, Sep-2020

Online published on Sep 30, 2020

Copyright © S. Jha, S. Gupta, Y. Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Opinion Mining or Sentiment analysis is a subfield of Natural Language Processing that is used to categorize documents, reviews, social media posts etc into different categories of opinion based on sentiment expressed in the text. Large datasets often collected from the worldwide web using different sources like social media, blogs, articles, news reports are used by various organizations for sentiment analysis, Thus better-performing models in terms of accuracy and speed are required which can be achieved through feature reduction. For the research, we have used supervised machine learning algorithms, a learning model that uses mutual information, chi-square and genetic algorithm for feature selection techniques and ensemble model for classification. We have done a comparative study on the feature selection techniques like mutual information, chi-square and union of mutual information gain and chi-square. The comparative study has been done using different feature selection techniques, accuracy and results using these feature selection techniques with different supervised learning algorithms are then compared. We have then used genetic algorithms for feature selection and classification and compared its result with above-mentioned feature selection techniques.

Key-Words / Index Term :
Opinion Mining, Feature Selection, Sentiment Analysis

References :
[1] Ketan Sarvakar, Urvashi K Kuchara, "Sentiment Analysis of movie reviews: A new feature-based sentiment classification", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.8-12, 2018
[2] B. Pang, L. Lee, and S. Vaithyanathain, “Thumbs up? Sentiment classification using machine learning techniques,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79–86, 2002.
[3] B. Pang and L. Lee, “A sentimental education: sentimental analysis using subjectivity summarization based on minimum cuts,” in Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 271–278, 2004.
[4] S.-M. Kim and E. Hovy, “Determining the sentiment of opinions,” in Proceedings of the 20th International Conference on Computational Linguistics, pp. 1367–1373, Association for Computational Linguistics, 2004.
[5] T. Mullen and N. Collier, “Sentiment analysis using support vector machines with diverse information sources,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 412–418, Barcelona, Spain, 2004.
[6] J. Wiebe, R. Bruce, M. Martin, T. Wilson, and M. Bell, Learning subjective language,” Computational Linguistics, vol. 30, no. 3, pp. 277–308, 2004.
[7] C. Zhang, W. Zuo, T. Peng, and F. He, “Sentiment classification for Chinese reviews using machine learning methods based on string kernel,” in Proceedings of the 3rd International Conference on Convergence and Hybrid Information Technology, pp. 909–914, November 2008.
[8] K. Dave, S. Lawrence, and D. M. Pennock, “Mining the peanut gallery: opinion extraction and semantic classification of product reviews,” in Proceedings of the 12th International Conference on WorldWideWeb (WWW `03), pp. 519–528, May 2003.
[9] M. Chau and J. Xu, “Mining communities and their relationships in blogs: a study of online hate groups,” International Journal of Human Computer Studies, vol. 65, no. 1, pp. 57–70, 2007.
[10] Q. Ye, Z. Zhang, and R. Law, “Sentiment classification of online reviews to travel destinations by supervised machine learning approaches,” Expert Systems with Applications, vol. 36, no. 3, pp. 6527–6535, 2009.
[11] Z. Zhang, Q. Ye, Z. Zhang, and Y. Li, “Sentiment classification of internet restaurant reviews written in cantonese,” Expert Systems with Applications, vol. 38, no. 6, pp. 7674–7682, 2011.
[12] Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).
[13] Mouthami K, Devi KN, Bhaskaran VM. Sentiment analysis and classification based on textual reviews. In: Information Communication and Embedded Systems. Piscataway: IEEE; 2013; p. 271–6.
[14] Li JJ, Yang H, Tang H. Feature mining and sentiment orientation analysis on product review. In: Management information and optoelectronic engineering. 2015. p. 79–4.
[15] Yang Y, Pedersen J. A comparative study on feature selection in text categorization. In: Proceedings of ICML-97, the 14th international conference on machine learning. 1997
[16] Teshome Hailemeskel Abebe, "The Derivation and Choice of Appropriate Test Statistic (Z, t, F and Chi-Square Test) in Research Methodology," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.6, pp.87-95, 2019
[17] A. Abbasi, H. Chen, and A. Salem, “Sentiment analysis in multiple languages: feature selection for opinion classification in Web forums,” ACM Transactions on Information Systems, vol. 26, no. 3, article 12, 2008.