Predicting Early Reviews for Effective Product Marketing on E-Commerce Websites

Authors

  • F. Femila  Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • S. Janakipriya  UG Scholar, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • R. B. Nivetha Sruthi  UG Scholar, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • S. Rohini  UG Scholar, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT195216

Keywords:

Arduino, Wi-Fi (ESP 8266), Load cell, Database System

Abstract

The percentage of purchasing products by the user has been increased drastically through web. Users even have the facility of sharing their thoughts about the particular product on web in the form of reviews, blogs, comments etc. Many users read review information given on web to take decisions for buying products. Some users may give the reviews for hyping the sale of the product or to decrease the sale. This may confuse the customers who rely on the reviews to buy a product. So, there is a need to find the honest reviews and remove fake reviews that are added by malicious or fraud user. The proposed system comes up with the solution for this problem. Leading events has been used to find the time interval between the reviews. The proposed system mines the active periods such as leading sessions to accurately locate the hierarchical fraud. These leading sessions can be useful for detecting the local anomaly instead of global anomaly of product reviews. After this to analyze the rating, reviews and hierarchy of the product we examine three facts, they are rating based facts, review based facts and hierarchy facts. In addition, we propose an optimization-based aggregation method to integrate all the facts for fraud detection. The evaluations of this optimization are done on synthetic dataset that are collected. The classified and summarized product review information helps web users to understand review contents easily in a short time.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

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How to Cite

[1]
F. Femila, S. Janakipriya, R. B. Nivetha Sruthi, S. Rohini, " Predicting Early Reviews for Effective Product Marketing on E-Commerce Websites , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.290-300, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195216