Open AccessEarly Reviewers Prediction and Spammer Detection on E-Commerce Websites
Author(s) -
Jai Kumar,
Palakusha Sirisha
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8493.118419
Subject(s) - helpfulness , spamming , ranking (information retrieval) , product (mathematics) , advertising , business , e commerce , computer science , marketing , world wide web , the internet , psychology , information retrieval , mathematics , social psychology , geometry
Reviews which are posted online play a vital part in present world as most of the customer's purchase items through an e-commerce website. Reviews which are posted on websites at an early stage known as early reviews, even though their contribution is very small their opinions determine new product's success and failure. Most of the spam reviews are written to improve their profit and promote their products and defame other products. In this system, the concentration is mainly on early reviews of the products and the products categories ranking on e-commerce websites i.e., Amazon. The analysis of reviews of product defines ratings of early reviewers’ and helpfulness scores of them are probably influencing product promotion additionally this model is enhanced with ranking and spammer detection.