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IJSTR >> Volume 8 - Issue 8, August 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Quantify The Reviewer Genunity Based On Behavior Metrics And Past Trust Analysis

[Full Text]

 

AUTHOR(S)

Pankaj Chaudhary, Dr. Anurag Aeron, Dr. Sandeep Vijay

 

KEYWORDS

Past trust analysis, behavior matrix, customer priority, deviation rate, bias rate, review similarity rate, review quality, relevance, content length, illustration, burst rate.

 

ABSTRACT

Internet has become easily accessible now days due to exponential growth of mobile and data networks. Smart phones have become easily accessible to a large number of people. This has made social networking an integral part of human life. People are sharing their comments and reviews on the forum or portal about their views and experiences. Even in taking the final decisions about the brand selections for best hotels, people are gradually depending on the previous online reviews. In such scenario, some companies may indulge themselves in generating the fake reviews with wrong intentions to create the positive or negative hype about the particular products. It may mislead the customers and decision makers. Several individual theories have been proposed by the researchers for fake review detection approaches, but effective integrated implementation is still underway. In this paper, some specific parameters are proposed to develop a robust model for identifying fake reviews and fake reviewers based on behavior matrix and past trust analysis. Although this work is specifically proposed for helping customers in selection of the best hotels by analyzing the previous online reviews, and help in concluding the right decision based on Location, Security, Price, Quality, Ambiance etc. Yet the something similar model may be designed after minor modifications for taking right decision in selecting the best colleges, best products etc.

 

REFERENCES

[1]. Xue H., Li F., Seo H. and Pluretti R., (2015), “Trust-Aware Review Spam Detection”, IEEE Computer Society Trustcom/BigDataSE/ISPA, pp. 726-733.

[2]. Fontanarava J., Pasi G. and Viviani M., (2017), “Feature Analysis for Fake Review Detection through Supervised Classification”, proceedings of IEEE International Conference on Data Science and Advanced Analytics, pp. 658-666.

[3] Liu P., Xu Z., Ai J. , Wang F., (2017), “Identifying Indicators of Fake Reviews Based on Spammer’s Behavior Features”, proceedings of IEEE International Conference on Software Quality, Reliability and Security, pp. 396- 403.

[4] Chauhan S.K., Goel A., Goel P., Chauhan A. and Gurve M.K., (2017), “Research on Product Review Analysis and Spam Review Detection”, proceedings of IEEE 4th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 399- 393.

[5] Christopher S.L. and Rahulnath H. A., (2016), “Review authenticity verification using supervised learning and reviewer personality traits”, proceedings of IEEE International Conference on Emerging Technological Trends, pp. 16- 23.

[6] Fei G., Mukherjee A., Liu1 B., Hsu M., Castellanos M., Ghosh R., (2013), “Exploiting Burstiness in Reviews for Review Spammer Detection” Proceedings of the Seventh International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Weblogs and Social Media, pp. 175- 185.

[7] Shojaee S., Azman A., Murad M., Sharef N. and Sulaiman N., (2017), “A Framework for Fake Review Annotation”, proceedings of 17th IEEE Computer Society UKSIM-AMSS International Conference on Modelling and Simulation, pp. 153- 159.

[8] Ahsan M.N.I., Nahian T., Kafi A.A., Hossain I., Shah F.M., (2017), “An Ensemble approach to detect Review Spam using hybrid Machine Learning Technique”, IEEE 19th International Conference on Computer and Information Technology, pp. 381- 388.

[9] Ahsan M.N.I., Nahian T., Kafi A.A., Hossain I., Shah F.M., (2016), “Review Spam Detection using Active Learning”, IEEE 19th International Conference on Computer and Information Technology, pp. 368- 375.

[10] Ohana B and Tierney B, “Sentiment classification of reviews using SentiWordNet” , 9th. IT & T Conference. 2009: pp 1232-1243

[11] Mudinas A., Zhang D. Levene M., “Combining lexicon and learning based approaches for concept-level sentiment analysis”, Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining. ACM, 2012: (5). pp 347-359.

[12] Jindal N., LIU B.. “Review spam detection”, Proceedings of the 16th international conference on World Wide Web. Canada. New York: ACM Press ,2007: pp 1189- 1190.

[13] Jindal N. and Liu B., ‘Opinion spam and analysis”, Proceedings of the 2008 International Conference on Web Search and Data Mining. New York: ACM Press , 2008: pp 219-230.

[14] Jindal N., Liu B., Lim E., et al. “ Finding unusual review patterns using unexpected rules”, Proceedings of the 19th ACM International Conference on Information and Knowledge Management. New York: ACM Press -2010 ; pp 1549-1552

[15] Wang G., Xie S., Liu B.,et al. “ Identify online store review spammers via social review graph” , ACM Transactions on Intelligent Systems and Technology, 2011,3(4):61.1-61.21.

[16 ] S. Feng, R. Banerjee, Y. Choi, “Syntactic Stylometry for Deception Detection”, ACL (2011), pp. 171-175.

[17] A. Mukherjee, B. Liu, N. Glance, “Spotting Fake Reviewer Groups in Consumer Reviews”, International Conference on World Wide Web ACM, 2012, pp. 191-200.

[18] G. Fei, A. Mukherjee, B. Liu, M. Hsu, M. Castellanos, R. Ghosh, R. “Exploiting Burstiness in Reviews for Review Spammer Detection”, ICWSM, 2013. pp 201-211

[19] A. Mukherjee, B. Liu, J. Wang, N. Glance, N. Jindal, “Detecting Group Review Spam”, International Conference on World Wide Web ACM, 2011, pp. 93-94.

[20] A. Mukherjee, A. Kumar, B. Liu, et al, “Spotting opinion spammers using behavioral footprints” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2013, pp. 632-640.

[21] F. Li, M. Huang, Y. Yang, X. Zhu, “Learning to Identify Review Spam” IJCAI, 2011, pp. 2488–2493.

[22] E.P. Lim, V.A. Nguyen, N. Jindal, B. Liu, H.W. Lauw, “Detecting product review spammers using rating behaviors”, Acm International Conference on Information & Knowledge Management ACM, 2012, pp. 939-948.

[23] T. Mikolov, I. Sutskever, K. Chen, et al, “Distributed Representations of Words and Phrases and their Compositionality”, Advances in Neural Information Processing Systems, 2013,vol. 26, pp. 3111-3119.

[24] B. Liu and L. Zhang. “A Survey of Opinion Mining and Sentiment Analysis”, Jour. Mining Text Data, 2012.

[25] Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, and S. Ma, “Explicit Factor Models for Explainable Recommendation based on Phraselevel Sentiment Analysis”, SIGIR, 2014.

[26] Settles, Burr. "Active learning literature survey." University of Wisconsin, Madison 52.55-66 (2010): 11.

[27] Feng, S., Xing, L., Gogar, A., and Choi, Y. "Distributional Footprints of Deceptive Product Reviews". ICWSM. 2012

[28] T. Elsayed, J. Lin, and D. W. Oard, "Pairwise document similarity in large collections with MapReduce," in Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, 2008, pp. 265-268.

[29] G. Esposito, LP-type methods for Optimal Transductive Support VectorMachines. Gennaro Esposito, PhD, 2014, vol. 3.

[30] P. Kalaivani and K. L. Shunmuganathan, "Sentiment classification of movie reviews by supervised machine learning approaches," Indian Journal of Computer Science and Engineering, vol. 4, no. 4, pp. 285-292, 2013.

[31] B. Pang and L. Lee, “A sentimental education: Sentiment analysis usingsubjectivity summarization based on minimum cuts,” in Proceedings ofthe 42nd annual meeting on Association for Computational Linguistics.Association for Computational Linguistics, 2004, p. 271. [Online]. Available from: http://www.cs.cornell.edu/People/pabo/movie%2Dreview%2Ddata/

[32] S. Hassan, M. Rafi, and M. S. Shaikh, “Comparing svm and naive bayesclassifiers for text categorization with wikitology as knowledge enrich-ment,” in Multitopic Conference (INMIC), 2011 IEEE 14th International.IEEE, 2011, pp. 31–34..

[33] C.-H. Chu, C.-A. Wang, Y.-C. Chang, Y.-W. Wu, Y.-L. Hsieh, and W.-L. Hsu, “Sentiment analysis on chinese movie review with distributedkeyword vector representation,” in Technologies and Applications ofArtificial Intelligence (TAAI), 2016 Conference on.IEEE, 2016, pp.84–89