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Fuzzy based Sentiment Analysis of Online Product Reviews using Machine Learning Techniques

by Haseena Rahmath P, Tanvir Ahmad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 17
Year of Publication: 2014
Authors: Haseena Rahmath P, Tanvir Ahmad
10.5120/17463-8243

Haseena Rahmath P, Tanvir Ahmad . Fuzzy based Sentiment Analysis of Online Product Reviews using Machine Learning Techniques. International Journal of Computer Applications. 99, 17 ( August 2014), 9-16. DOI=10.5120/17463-8243

@article{ 10.5120/17463-8243,
author = { Haseena Rahmath P, Tanvir Ahmad },
title = { Fuzzy based Sentiment Analysis of Online Product Reviews using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 17 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 9-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number17/17463-8243/ },
doi = { 10.5120/17463-8243 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:28:26.364776+05:30
%A Haseena Rahmath P
%A Tanvir Ahmad
%T Fuzzy based Sentiment Analysis of Online Product Reviews using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 17
%P 9-16
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The exponential growth of internet opened a new platform where people can freely express and exchange their suggestions, ideas and feedback about any product or services. People prefer e-commerce websites to buy or sell products or services and they like to review and analyze the opinions of others while purchasing any product or services. The social-medias, e-commerce websites, review websites, forums, blogs etc. encourage users to share their views, opinions, suggestions and feedback about different aspects that touch their day to day life. This trend lead to a huge accumulation of user generated content on internet. The processing and analyzing this huge unstructured content, which are written in natural language is a challenging task. These factors motivated the development of an opinion mining and sentiment analysis system that can automatically extract, classify and summarize users' reviews. The present work proposes a multi-step opinion mining system that involves pre-processing to clean the document, a rule-based system to extract features and a scoring mechanism to tag their polarity. The proposed system can be used for binary as well as fine-grained sentiment classification of user reviews. The proposed technique utilizes fuzzy functions to emulate the effect of various linguistic hedges such as dilators, concentrator and negation on opinionated phrases that make the system more accurate in sentiment classification and summarization of users' reviews. Experimental evaluation indicates the system can perform the sentiment analysis with an accuracy of 93. 85 %.

References
  1. S. R. Das and M. Y. Chen. "Yahoo! for amazon: Sentiment extraction from small talk on the web", Management Science, 53(9):1375–1388, 2007.
  2. Sentiment Analysis - Wikipedia, the free encyclopedia. Retrieved on December 1, 2013, from the World Wide Web: http://en. wikipedia. org/wiki/Sentiment_analysis
  3. V. P. H. Binali and W. Chen. "A state of the art opinion mining and its application domains", In IEEE International Conference on Industrial Technology, pages 1–6, February 2009.
  4. Bing Liu. (2010), "Sentiment Analysis and Subjectivity", Handbook of Natural Language Processing, Second Edition by N. Indurkhya and F. J. Damerau.
  5. Kushal Dave, Steve Lawrence, and David M. Pennock. (2003), "Mining the peanut gallery: Opinion extraction and semantic classification of product reviews", In Proceedings of WWW. Pages: 519–528.
  6. M. Hu and Bing Liu. (2004), "Mining and summarizing customer reviews", Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data, Aug. 22-25, ACM Press, Washington, USA. , pp: 168-177.
  7. Dunja Mladenic. (1999), "Text-Learning and Related Intelligent Agents: A Survey", In Intelligent Systems and their applications, IEEE, Vol. 14 Issue 4. Pages: 44-54.
  8. C. M. Kristina Toutanova, Dan Klein and Y. Singer. "Feature-rich partof-speech tagging with a cyclic dependency network". In HLT-NAACL, pages 252–259. ACM, 2003.
  9. Ms. K. Mouthami, Ms. K. Nirmala Devi, Dr. V. Murali Bhaskaran, "Sentiment Analysis and Classification Based On Textual Reviews", Information Communication and Embedded Systems (ICICES), 2013 International Conference on 21-22 Feb. 2013Page(s): 271 – 276.
  10. A. Kennedy and D. Inkpen, "Sentiment classification of movie reviews using contextual valence shifters", Computational Intelligence, vol. 22, no. 2, pp. 110–125, 2006.
  11. L. Polanyi and A. Zaenen, "Contextual valence shifters", in Computing Attitude and Affect in Text: Theory and Applications, vol. 20 of The Information Retrieval Series, pp. 1–10, 2006.
  12. A. D. Vo and C. Y. Ock, "Sentiment classification: a combination of PMI, SentiWordNet and fuzzy function", in Proceedings of the 4th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI '12), vol. 7654, part 2 of Lecture Notes in Computer Science, pp. 373–382, 2012.
  13. S. Nadali, M. A. A. Murad, and R. A. Kadir, "Sentiment classification of customer reviews based on fuzzy logic", in Proceedings of the International Symposium on Information Technology (ITSim' 10), pp. 1037–1044, mys, June 2010.
  14. M. Hu and B. Liu, "Mining and summarizing customer reviews", Proceedings of the tenth ACM international conference on Knowledge discovery and data mining, Seattle, 2004, pp. 168-177.
  15. B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: sentiment classification using machine learning techniques", Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol. 10, 2002, pp. 79-86.
  16. K. Dave, S. Lawrence, and D. M. Pennock, "Mining the peanut gallery: Opinion extraction and semantic classification of product reviews", Proceedings of WWW, 2003, pp. 519–528.
  17. R. Prabowo and M. Thelwall, "Sentiment analysis: A combined approach", Journal of Informetrics, vol. 3, pp. 143-157, 2009.
  18. B. Pang and L. Lee, "Opinion mining and sentiment analysis", Foundations and Trends in Information Retrieval 2(1-2), 2008, pp. 1–135.
  19. A. Harb, M. Planti, G. Dray, M. Roche, Fran, o. Trousset and P. Poncelet, "Web opinion mining: how to extract opinions from blogs?", presented at the Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology, Cergy-Pontoise, France, 2008.
  20. P. Turney, "Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews", Proceedings of the Association for Computational Linguistics (ACL), 2002, pp. 417–424.
  21. Z. Wang, Y. He, and M. Jiang, "A comparison among three neural networks for text classification", in Proceedings of the 8th International Conference on Signal Processing (ICSP '06), pp. 1883–1886, November 2006.
  22. R. D. Goyal, "Knowledge based neural network for text classification", in Proceedings of the IEEE International Conference on Granular Computing (GrC '07), pp. 542–547, November 2007.
  23. L. Dey and Sk. M. Haque, "Opinion mining from noisy text data", International Journal on Document Analysis and Recognition, vol. 12, no. 3, pp. 205–226, 2009.
  24. D. Isa, L. H. Lee, V. P. Kallimani, and R. Rajkumar, "Text document preprocessing with the bayes formula for classification using the support vector machine", IEEE Transactions on Applied Computational Intelligence and Soft Computing 9 Knowledge and Data Engineering, vol. 20, no. 9, pp. 1264–1272, 2008.
  25. M. K. Dalal and M. A. Zaveri, "Semisupervised learning based opinion summarization and classification for online product reviews", Applied Computational Intelligence and Soft Computing,vol. 2013,Article ID 910706, 8 pages, 2013.
  26. L. Zhao and C. Li, "Ontology based opinion mining for movie reviews", in Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management, pp. 204–214, 2009.
  27. D. Sleator and D. Temperley, "Parsing english with a link grammar", in Proceedings of the 3rd International Workshop on Parsing Technologies, pp. 1–14, 1993.
  28. S. Shi and Y. Wang, "A product features mining method based on association rules and the degree of property co-occurrence", in Proceedings of the International Conference on Computer Science and Network Technology , vol. 2, pp. 1190–1194, December 2011.
  29. C. -P. Wei,Y. -M. Chen,C. -S. Yang, and C. C. Yang, "Understanding what concerns consumers: a semantic approach to product feature extraction fromconsumer reviews", Information Systems and e-Business Management, vol. 8, no. 2, pp. 149–167, 2010.
  30. W. Zhang, T. Yoshida, and X. Tang, "Text classification using multi-word features", in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC '07), pp. 3519–3524, October 2007.
  31. M. K. Dalal and M. A. Zaveri, "Automatic text classification of sports blog data", in Proceedings of the Computing, Communications and Applications Conference, pp. 219–222, January 2012.
  32. W. Zhang, T. Yoshida, and X. Tang, "TFIDF, LSI andmulti-word in information retrieval and text categorization", in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '08), pp. 108–113, October 2008.
  33. Ahmad Kamal, Muhammed Abulaish and Tarique Anwar. "Mining Feature-Opinion Pairs and Their Reliability Scores from Web Opinion Sources", in ACM 978-1-4503-0915,2012.
  34. Abulaish Muhammad, Jahiruddin, M. N. Doja and T. Ahmad, "Feature and Opinion Mining for Customer Review Summarization", in Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence (PReMI 09), LNCS 5909, pp 219-224, 2009.
  35. V. Hatzivassiloglou and K. Mckeown, "Predicting the semantic orientation of adjectives", in Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics (ACL '98), pp. 174–181, 1998.
  36. P. D. Turney and M. L. Littman, "Measuring praise and criticism: inference of semantic orientation fromassociation", ACM Transactions on Information Systems, vol. 21, no. 4, pp. 315–346, 2003.
  37. G. A. Miller, "WordNet: a lexical database for English", Communications of the ACM, vol. 38, no. 11, pp. 39–41, 1995.
  38. S. Baccianella, A. Esuli, and F. Sebastiani, "SentiWordNet 3. 0: an enhanced lexical resource for sentiment analysis and opinion mining", in Proceedings of the 7th International Conference on Language Resources and Evaluation, pp. 2200–2204,2010.
  39. L. A. Zadeh, "The concept of a linguistic variable and its application to approximate reasoning-II", Information Sciences, vol. 8, no. 4, part 3, pp. 301–357, 1975.
  40. T. Zamali, M. A. Lazim, andM. T. A. Osman, "Sensitivity analysis using fuzzy linguistic hedges", in Proceedings of the IEEE Symposium on Humanities, Science and Engineering Research, pp. 669–672, 2012.
  41. The Stanford Parser: A statistical parser, Available at: http://nlp. stanford. edu/software/lex-parser. shtml
  42. Penn Treebank Tagset, Retrieved on March 15, 2014, from the World Wide Web: http://www. computing. dcu. ie/~acahill/tagset. html
Index Terms

Computer Science
Information Sciences

Keywords

Opinion Mining Sentiment Analysis Machine Learning Techniques Rule Based System Online Product Reviews Review Classification Review Summarization

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