Performance Evaluation of Feature Selection Method for Sentiment Classification of Online Reviews Using Machine Learning Techniques


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Abstract


Large volumes of data are available in the web. The discussion forum, review sites, blogs and news corpora are some of the opinion rich resources. More importantly, the information in these forms and reviews are important for both customers and product manufactures, to find the strength and weakness of the product. The information in the Internet is overloaded for customers and unable to read all the reviews and available information. In this study, we evaluate the performance for sentiment classification of online reviews in term of accuracy, precision and recall. We compared three supervised machine learning algorithms of NaivieBayes, Support vector machine and k-NN model for sentiment classification of movie reviews with a size of 2000 documents. The experimental finding indicated that the SVM approach outperformed than the NavieBayes and k-NN approaches, when the number of feature selected about 2000. The SVM approach reached accuracy of more than 83%. We have shown that the number of feature selected between 100 and 1500 for better results.
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Keywords


Movie Review; Opinions; Online Reviews; Sentiment Classification; Supervised Machine Learning Algorithms

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References


B. Pang, et al (July 2002)., “Thumbs up? Sentiment Classification Using Machine Learning Techniques,” Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), ACL Press, pp 79-86.

P.D. Turney (July 2002) “Thumbs up or Thumbs down? Semantic Orientation Applied to Unsupervised Classification of Reviews,” Proc. of the 40th Annual Meetings of the Association for Computational Linguistics, ACL Press, pp 417-424.

J. Wiebe, et al (2004)., “Learning Subjective Language,” The Association for Computational Linguistics, vol. 30, no. 3, pp. 277-308.

R. Mukras, J. Carroll (2004). A comparison of machine learning techniques applied to sentiment classification, pp 200-204.

Mori Rimon (2004), “Sentiment Classification: Linguistic and Non-linguistic Issues”, pp 444-446.

Alec Go (2005), “Twitter Sentiment Classification using Distant Supervision”, Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), Vancouver, CA.

Changli Zhang, Wanli Zuo, Tao Peng,Fengling He (2008), “Sentiment Classification for Chinese Reviews Using Machine Learning Methods Based on String Kernel”, Third 2008 International Conference on Convergence and Hybrid Information Technology.

Kudo et al (2001). “An operational system for detecting and tracking opinions in on-line discussion”. In SIGIR Workshop on Operational Text Classification, pp 449-454.

Dave K Lawrence, Pennock (2003), D. M. Mining, “opinion extraction and semantic classification of product reviews”, In Proceedings of the 12th international WWW conference, pp. 519-528, Hungary.

Tony Mullen and Nigel Collier (July 2004) , “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.

Yan Dang, Yulei Zhang, Hsinchun ChenA (July 2010), “Lexicon Enhanced Method for Sentiment Classification: An Experiment on Online Product Reviews”, Department of Management Information Systems, vol. 25, no. 4, pp. 46-53.

Long-Sheng Chen and Hui-Ju Chiu( March2009), “Developing a Neural Network based Index for Sentiment Classification”, Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong Kong, pp 744-749.

Tao, J. and Tan (Oct 2004), T Emotional Chinese talking head system. The 6th International Conference on Multimodal Interface, pp 273-280.

Hu, M. and Liu (2004), B, “Mining and summarizing customer reviews”. pp. 755–760.

Subasic P, Huettner A (2001), “Affect analysis of text using fuzzy semantic typing”, IEEE Transactions on Fuzzy Systems, vol. 9, no. 4, pp 483-496.

S. Chandrakala And C. Sindhu Issn: 2229-6956(Online) Ictact Journal On Soft Computing, October 2012, Volume: 03, Issue: 01 “Opinion Mining And Sentiment Classification: A Survey”.


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