Abstract
In recent times, e-commerce sites have become an essential part of people lifestyle. Viewers give feedback and firsthand account of the online products, and these reviews thus play an important role in decision making of the other buyers. So, in order to increase or decrease sales of products, spam reviews are generated by the companies. Hence, there is a need to detect and filter the spam reviews to provide customers genuine reviews of the product. In this paper, a review processing method is proposed. Some parameters have been suggested to find the usefulness of reviews. These parameters show the variation of a particular review from other, thus increasing the probability of it being spam. This method introduced classifies the review as helpful or non-helpful depending on the score assigned to the review.
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References
Y. Lin, T. Zhu, X. Wang, J. Zhang, and A. Zhou, “Towards online review spam detection,” in Proceedings of the 23rd International Conference on World Wide Web. ACM, 2014, pp. 341–342.
A. Heydari, M. ali Tavakoli, N. Salim, and Z. Heydari, “Detection of review spam: A survey,” Expert Systems with Applications, vol. 42, no. 7, pp. 3634–3642, 2015.
S. Xie, G. Wang, S. Lin, and P. S. Yu, “Review spam detection via temporal pattern discovery,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012, pp. 823–831.
Y. Lin, T. Zhu, H. Wu, J. Zhang, X. Wang, and A. Zhou, “Towards online anti-opinion spam: Spotting fake reviews from the review sequence,” in Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on. IEEE, 2014, pp. 261–264.
E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw, “Detecting product review spammers using rating behaviors,” in Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, 2010, pp. 939–948.
G. Wang, S. Xie, B. Liu, and P. S. Yu, “Identify online store review spammers via social review graph,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 3, no. 4, p. 61, 2012.
M. Jiang, P. Cui, and C. Faloutsos, “Suspicious behavior detection: Current trends and future directions,” IEEE Intelligent Systems, vol. 31, pp. 31–39, 2016.
M. Sasaki and H. Shinnou, “Spam detection using text clustering,” in Cyberworlds, 2005. International Conference on. IEEE, 2005.
R. Patel and P. Thakkar, “Opinion spam detection using feature selection,” in Computational Intelligence and Communication Networks (CICN), 2014 International Conference on. IEEE, 2014, pp. 560–564.
X. Li and X. Yan, “A novel chinese text mining method for e-commerce review spam detection,” in International Conference on Web-Age Information Management. Springer, 2016, pp. 95–106.
L. Wu, X. Hu, F. Morstatter, and H. Liu, “Adaptive spammer detection with sparse group modeling,” 2017.
J. G. Thanikkal and M. Danish, “A novel approach to improve spam detection using sds algorithm,” International Journal for Innovative Research in Science and Technology, vol. 1, no. 12, pp. 306–310, 2015.
S. Rayana and L. Akoglu, “Collective opinion spam detection: Bridging review networks and metadata,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015, pp. 985–994.
Y. Shao, M. Trovati, Q. Shi, O. Angelopoulou, E. Asimakopoulou, and N. Bessis, “A hybrid spam detection method based on unstructured datasets,” Soft Computing, vol. 21, pp. 233–243, 2017.
J. K. Rout, S. Singh, S. K. Jena, and S. Bakshi, “Deceptive review detection using labeled and unlabeled data,” Multimedia Tools and Applications, pp. 1–25, 2016.
D. H. Fusilier, M. Montes-y Gómez, P. Rosso, and R. G. Cabrera, “Detection of opinion spam with character n-grams,” in International Conference on Intelligent Text Processing and Computational Linguistics. Springer, 2015, pp. 285–294.
M. I. Ahsan, T. Nahian, A. A. Kafi, M. I. Hossain, and F. M. Shah, “Review spam detection using active learning,” in Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2016 IEEE 7th Annual. IEEE, 2016, pp. 1–7.
H. Xue, F. Li, H. Seo, and R. Pluretti, “Trust-aware review spam detection,” in Trustcom/BigDataSE/ISPA, 2015 IEEE, vol. 1. IEEE, 2015, pp. 726–733.
G. Ansari, T. Ahmad, and M. Doja, “Review ranking method for spam recognition,” in Contemporary Computing (IC3), 2016 Ninth International Conference on. IEEE, 2016, pp. 1–5.
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Ghai, R., Kumar, S., Pandey, A.C. (2019). Spam Detection Using Rating and Review Processing Method. In: Panigrahi, B., Trivedi, M., Mishra, K., Tiwari, S., Singh, P. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 670. Springer, Singapore. https://doi.org/10.1007/978-981-10-8971-8_18
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DOI: https://doi.org/10.1007/978-981-10-8971-8_18
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