A Graph-Based Approach for Aspect Extraction from Online Customer Reviews

Abstract

E-commerce websites have become main market players in the 21st century due to advancement in the internet technology. Apart from buying products online, customers are also providing reviews on the products purchased by them. These reviews help new customers to buy various products according to their needs, liking, and preferences. However, millions of reviews are added by the customer on a daily basis. To extract meaningful information manually from these huge amounts of reviews is a tough task. So, it is required to develop an automatic analytics tool for the review sentences. Aspect extraction is one of the vital tasks in the process of meaningful information extraction from the products having various entities. In this work, a novel product aspect extraction approach has been proposed which utilize a graphbased technique with the integration of statistical and semantic information. The analysis of experimental results shows that the proposed approach is efficient and effective in comparison to the state of art methods. Subject Categories and Descriptors [H.2.8 Database Applications]: Data mining; [K.4.4 Electronic Commerce] General Terms: E-Commerce, Customer Reviews, Opinion mining