Spam Review Detection and Removal in E-Commerce Website

Authors(5) :-D. Janupriya, S. Vallimayil, N. Sujanthini, V. Nandhini, A. T. Barani Vijayakumar

In this era, most of the people are eager to purchase products via online e-commerce sites such as Flipkart, Amazon, Paytm., etc. Before purchasing any products via online people spend some time for analyzing the available comments such as feedbacks, reviews and ratings related to the product which are posted by other customers. Based on these comments, people took a decision whether to buy that product or not. If the given comments are positive, then there is a chance to buy the product. If not, people may ignore the product. On the other hand, these reviews play a vita role in the improvement of a business. Positive reviews can add profit to a company. While negative reviews can potentially impact credibility and cause economic loss to a company. For gaining profit to a company or making a company reputation down, imposters namely spammers were encouraged to write fake reviews about a company product. These fake reviews can mislead the customers in taking decision. If the fake reviews are positive, then most of the customers trust them and buy the product there by improve a company profit rate. Where as in case of negative reviews, the customers won’t buy the product there by damage the reputation of a company. Therefore, it is most important to detect the spammer and spam content to make online purchase trustworthy. In the past few years, several approaches have been proposed to deal with the problem but there are still some millstones for detecting the spammer and spam content correctly. The proposed framework includes two methodologies namely, Rate Deviation using Threshold and User Behavioral method for identifying spammer and spam contents in an effective manner. In Rate Deviation using Threshold method, it calculates a score value against each user rating and overall rating of a product. Whereas in User Behavioral method, it calculates the user burst time.

Authors and Affiliations

D. Janupriya
B.E Scholar, Department of Computer Science and Engineering, Saranathan Engineering College, Panjapur, Trichy, Tamil Nadu, India
S. Vallimayil
B.E Scholar, Department of Computer Science and Engineering, Saranathan Engineering College, Panjapur, Trichy, Tamil Nadu, India
N. Sujanthini
B.E Scholar, Department of Computer Science and Engineering, Saranathan Engineering College, Panjapur, Trichy, Tamil Nadu, India
V. Nandhini
B.E Scholar, Department of Computer Science and Engineering, Saranathan Engineering College, Panjapur, Trichy, Tamil Nadu, India
A. T. Barani Vijayakumar
Assistant Professor, Department of Computer Science and Engineering, Saranathan Engineering College, Panjapur, Trichy, Tamil Nadu, India

E- Commerce Site, Spammer, Spam Content, Positive Review, Negative Review, Score, Threshold, Burst Time.

  1. Prof Saeedreza Shehnepoor, Mostafa Salehi, Reza Farahbakhsh, Noel Crespi, NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media, 2017.
  2. A. j. Minnich, N. Chavoshi, A. Mueen, S. Luan, and M. Faloutsos. Trueview,Harnessing the power of multiple review sites.2015.
  3. B. Viswanath, M. Ahmad Bashir, M. Crovella, S. Guah, K. P. Gummadi, B. Krishnamurthy, and A. Mislove.Towards detecting anomalous user behavior in online social networks. ,2014.
  4. H. Li, Z. Chen, B. Liu, X. Wei, and J. Shao.,Spotting fake reviews via collective PU learning,2014.
  5. Geli Fei, Arjun Mukherjee , Bing Liu , Meichun Hsu, Malu Castellanos, Riddhiman Ghosh . Exploiting Burstiness in Reviews for Review Spammer Detection, 2015
  6. Satish Tukaram Pokharkar, Ajit Jaysingrao Shete, Vishal Dyandeo Ghogare,Survey in Online social media Skelton by network based spam,2017
  7. Snehal Dixit1 & A.J.Agrawal,Survey on Review Spam Detection, 2013.
  8. Kolli Shivagangadhar, Sagar H, Sohan Sathyan, Vanipriya C.H,Fraud Detection in Online Reviews using Machine Learning Techniques, 2015
  9. Sunita Mahajan1, Dr. Vijay Rana ,Spam Detection on Social Network through Sentiment Analysis,2017
  10. Sushant Kokate, Bharat Tidke ,Fake Review and Brand Spam Detection using J48 Classifier ,2015
  11. Siddu P. Algur, Jyoti G. Biradar ,Opinion Mining and Review Spam Detection: Issues and Challenges ,2017
  12. Ahmed Abu Hammad ,Alaa El-Halees ,An Approach for Detecting Spam in Arabic Opinion Reviews,2015

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 754-758
Manuscript Number : CSEIT1833225
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

D. Janupriya, S. Vallimayil, N. Sujanthini, V. Nandhini, A. T. Barani Vijayakumar, "Spam Review Detection and Removal in E-Commerce Website", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.754-758, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833225

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