Performance Evaluation of Machine Learning Classifiers in Sentiment Mining
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - June Issue 2013 by IJCTT Journal|
|Volume-4 Issue-6 |
|Year of Publication : 2013|
|Authors :G.Vinodhini, RM.Chandrasekaran|
G.Vinodhini, RM.Chandrasekaran"Performance Evaluation of Machine Learning Classifiers in Sentiment Mining "International Journal of Computer Trends and Technology (IJCTT),V4(6):1783-1786 June Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - In recent years, the use of machine learning classifiers is of great value in solving a variety of problems in text classification. Sentiment mining is a kind of text classification in which, messages are classified according to sentiment orientation such as positive or negative. This paper extends the idea of evaluating the performance of various classifiers to show their effectiveness in sentiment mining of online product reviews. The product reviews are collected from Amazon reviews. To evaluate the performance of classifiers various evaluation methods like random sampling, linear sampling and bootstrap sampling are used. Our results shows that support vector machine with bootstrap sampling method outperforms others classifiers and sampling methods in terms of misclassification rate.
. B. Liu, M. Hu, and J. Cheng, “Opinion Observer: Analyzing and Comparing Opinions on the Web,”Proc. International World Wide Web Conference, Japan, May 2005, pp. 342-351
. Ho, Tin Kam. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8): 832- 844.
. Kang Liu, Jun Zhao. “NLPR at Multilingual Opinion Analysis Task in NTCIR7”, Proceeding of NTCIT-7 workshop Meeting. 2008. pp:226-231.
. Pang, B., and Lee, L. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In ACL, 271–278.
. Polikar, R. 2006. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine Third Quarter:21–45.
. Popescu,O Etzioni. “Extracting Product Features and Opinions from Reviews,” Proceedings of Empirical Methods in Natural Language Processing, 2005. pp.339- 346
. Schapire, R. E. 2002. The boosting approach to machine learning: Anoverview.
. Shu Zhang, Wen-Jie Jia, Ying-Ju Xia, Yao Meng, Hao Yu, “Opinion Analysis of Product Reviews”, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp.591-595
. Snyder, B., and Barzilay, R. 2007. Multiple aspect ranking using the good grief algorithm. In Proceedings of NAACL HLT, 300–307.
.Tetsuya Nasukawa and Jeonghee Yi, “Sentiment analysis: Capturing favorability using natural language processing,” In Proc. of the Second International Conferences on Knowledge Capture, 2003. pp.70-77.
.Whitelaw, C.; Garg, N.; and Argamon, S. 2005. Using appraisal groups for sentiment analysis. In Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management, Bremen,Germany, October 31 - November 5, 2005,625– 631. ACM.
Keywords -sentiment, mining, classification, machine learning, support vector.