Mining Customers' Opinions for Online Reputation Generation and Visualization in e-Commerce Platforms
This addresses the challenge for online shoppers and sellers in efficiently processing large volumes of reviews, though it appears incremental as it builds on existing mining and visualization techniques.
The research tackled the problem of overwhelming customer reviews in e-commerce by developing reputation systems that automatically mine and visualize opinions to aid decision-making.
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.