Feature extraction and analysis of onlinereviews for the recommendation of booksusing opinion mining technique

Abstract—Abstract The customer’s review plays an important role in deciding the purchasing behavior for online shopping as a customer prefers to get the opinion of other customers by observing their opinion through online products’ reviews, blogs and social networking sites, etc. The customer’s reviews reflect the customer’s sentiments and have a substantial significance for the products being sold online including electronic gadgets, movies, house hold appliances and books. Hence, extracting the exact features of the products by analyzing the text of reviews requires a lot of efforts and human intelligence. In this paper we intend to analyze the online reviews available for books and extract book-features from the reviews using human intelligence. We have proposed a technique to categorize the features of books from the reviews of the customers. The extracted features may help in deciding the books to be recommended for readers. The ultimate goal of the work is to fulfill the requirement of the user and provide themtheir desired books. Thus, we have evaluated our categorization method by users themselves,and surveyed qualified persons for the concerned books. The survey results show high precision of the features categorized which clearly indicates that proposed method is very useful and appealing. The proposed technique may help in recommending the best books for concerned people and may also be generalized to recommend any product to the users.© 2016 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license