Mining Customers' Opinions for Online Reputation Generation and Visualization in e-Commerce Platforms

TL;DR

This paper discusses methods for automatically extracting and visualizing customer opinions from online reviews to assist buyers and sellers in e-commerce, especially when dealing with large volumes of data.

Contribution

It proposes a system for mining and visualizing customer reviews to generate online reputation insights, aiding decision-making in e-commerce platforms.

Findings

Developed a reputation system for review analysis

Enhanced visualization tools for customer opinions

Improved decision support for online shoppers

Abstract

Customer reviews represent a very rich data source from which we can extract very valuable information about different online shopping experiences. The amount of the collected data may be very large especially for trendy items (products, movies, TV shows, hotels, services...), where the number of available customers' opinions could easily surpass thousands. In fact, while a good number of reviews could indeed give a hint about the quality of an item, a potential customer may not have time or effort to read all reviews for the purpose of making an informed decision (buying, renting, booking...). Thus, the need for the right tools and technologies to help in such a task becomes a necessity for the buyer as for the seller. My research goal in this thesis is to develop reputation systems that can automatically provide E-commerce customers with valuable information to support them during their online decision-making process by mining online reviews expressed in natural language.