A Secure and Efficient Product Analysis for Trust based E-Commerce Application

Abstract

In current situation, e-commerce is considered essential and a growing business in day today life. People are buying the product based on deals and offers provided in the ecommerce application. Customer does not know about the quality and authenticity of the product, and sometimes the seller does not meet the customer requirements. To avoid this situation, a Secure Trust Monitoring KNN (STRUMKNN) ecommerce model has been implemented in this research. This model consists of a customer trust reputation system based on feedback comments and rating system given by each client. This model helps customer to find the good seller in ecommerce sites based on trust weight to find the product quality and product delivery. In the proposed model, two techniques such as word separation method and sentiment analysis are employed for providing trust profile to the customer. Here, based on the key pair of words either positive or negative given by a customer in the comments section, the suggestion is given to the buyer to order the product on trust profile. Sentiment analysis helps to identify if the customer ratings given by the user are accurate or not, based on the ratings and trust. The experimental results prove that the sentimental assessment used in the suggested model provides customers a successful way of finding the product and increases the confidence of e-commerce application users.