Gathering user reviews for an opinion dictionary

Abstract

Nowadays, people purchase a lot of products from online shopping sites. To support customers in decision making, some sites collect and provide user reviews on products. However, contents of the user reviews are too abundant for customers to analyze them in a short period of time. The automatic analysis of reviews is important to provide users with valuable information about goods of any category. The objective of this research is to improve the usefulness of reviews for consumers. This research focuses on an opinion dictionary as a collection of specific keywords and key phrases. This opinion dictionary models a standardized better review to extract patterns of trustworthy reviews. In this study, a simple corpus of three different categories of goods is composed. It consists of noun and adjective keywords. This research is successful to obtain essential features and relations among three different categories in the opinion dictionary. Moreover, this opinion dictionary will be applied to supervised learning methods, such as a support vector machine to create a review evaluation system. The findings from this study can contribute to assist users' decisions to evaluate reliable and useful reviews.