Feature Based Summarization of Customers' Reviews of Online Products

With the growing availability and popularity of opinion-rich resources such as review forums for the product sold online, choosing the right product from a large number of products have become difficult for the user. For trendy product, the number of customers' opinions available can be in the thousands. It becomes hard for the customers to read all the reviews and if he reads only a few of those reviews, then he may get a biased view about the product. Makers of the products may also feel difficult to maintain, keep track and understand the customers' views for the products. Several research works have been proposed in the past to address these issues, but they have certain limitations: The systems implemented are completely opaque, the reviews are not easier to perceive and are time consuming to analyze because of large irrelevant information apart from actual opinions about the features, the feature based summarization system that are implemented are more generic ones and static in nature. In this research, we proposed a dynamic system for feature based summarization of customers' opinions for online products, which works according to the domain of the product. We are extracting online reviews for a product on periodic bases, each time after extraction, we carry out the following work: Firstly, identification of features of a product from customers' opinions is done. Next, for each feature, its corresponding opinions' are extracted and their orientation or polarity (positive/negative) is detected. The final polarity of feature-opinions pairs is calculated. At last, feature based summarizations of the reviews are generated, by extracting the relevant excerpts with respect to each feature-opinions pair and placing it into their respective feature based cluster. These feature based excerpts can easily be digested by the user. (C) 2013 The Authors. Published by Elsevier B.V.