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An Efficient Hybrid Approach to Detect Spam in Product based User Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 41 |
| Year of Publication: 2018 |
| Authors: Avishi Kansal, Hari Shankar Aggarwal, Aatif Jamshed |
10.5120/ijca2018917075
|
Avishi Kansal, Hari Shankar Aggarwal, Aatif Jamshed . An Efficient Hybrid Approach to Detect Spam in Product based User Review. International Journal of Computer Applications. 180, 41 ( May 2018), 11-18. DOI=10.5120/ijca2018917075
Abstract
All e-commerce sites provide facility to the users for giving views and experience of the product and services they experienced. The customer’s reviews are increasingly used by individuals, manufacturers and retailers for purchase and business decisions. As there is no scrutiny over the reviews received, spammers produce synthesized reviews to promote some products/brand and demote competitors’ products/brand for profit or publicity. As the amount of spam has been increased tremendously using bulk mailing tools, there is an emerging need for spam detection. In this paper we propose an optimal approach to detect spam reviews based on number of reviews posted per day from a particular IP address and geographic location. In case of spam, it blocks the spammer’s IP and also send a mail intimation to give an alert. It performs feature extraction based on the authentic reviews and also provides a star rating system. In our work we have combined LSVD and LSI algorithms to guarantee very high detection rates as well as feature extraction facility. Other concepts like ontology, spam dictionary, sentiment analysis, indexing, decision tree, opinion mining, clustering have also been included to provide the most efficient approach.
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