Product Ranking System in E-Commerce Website for Validation using Sentimental Analysis

Jayasree J; Malini S; Sri Amitha Mathi M; Bavithra E

CALL FOR PAPERS : Feb-2026

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25-Feb-26

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Product Ranking System in E-Commerce Website for Validation using Sentimental Analysis

Author(s):

Jayasree J , Pondicherry Engineering College; Malini S, Pondicherry Engineering College; Sri Amitha Mathi M, Pondicherry Engineering College; Bavithra E, Pondicherry Engineering College

Keywords:

Data Analytics, Graphical representation, Naïve Bayes classifier, Product Recommendation, Product Recommendation, Support Vector Machine and Sentiment polarity

Abstract:

In today’s technological advanced world many e-commerce websites like Amazon, Snapdeal and Flipkart, and other online shopping sites tend to collect product reviews from customers to ascertain the satisfaction level on specific products. Data analysis are applied on product reviews in order to bring about useful analytical information in the form of statistics that can help people working in an organization for business analysis in making high end decisions in order to seek out the demand of customer against their existing business competitors. In Product Ranking System, reviews plays vital role in determining customer satisfaction as well as market trend for that particular product, say in case in terms of electronic products to get relevant data in less time. The proposed system provides summary of reviews for an electronic products by classifying these products as positive, negative or neutral. The proposed system is a web application that takes product reviews from customers as input and performs Classification Analysis (CA) on the reviews to categorize reviews as positive and negative feedbacks based on both the structured or unstructured input reviews given by the user, performs pre-processing, calculates polarity of reviews extracts features of the given electronic product, validate review by analyzing the satisfactory level of customer and plot the result in the form of graph. This system analysis helps both manufacturers to predict public opinion of their product and also customers to make better decisions and in order to obtain improved services.