Title :
Recommending products to customers using opinion mining of online product reviews and features
Author :
Rajeev, P. Venkata ; Rekha, V. Smrithi
Abstract :
E-Commerce sites are gaining popularity across the world. People visit them not just to shop products but also to know the opinion of other buyers and users of products. Online customer reviews are helping consumers to decide which products to buy and also companies to understand the buying behavior of consumers. In this paper we have created a prototype Web based system for recommending and comparing products sold online. We have used natural language processing to automatically read reviews and used Naive Bayes classification to determine the polarity of reviews. We have also extracted the reviews of product features and the polarity of those features. We graphically present to the customer, the better of two products based on various criteria including the star ratings, date of review, the helpfulness score of the review and the polarity of reviews.
Keywords :
Bayes methods; Web sites; consumer behaviour; data mining; electronic commerce; natural language processing; pattern classification; recommender systems; retail data processing; Web based system; buyer opinion; customer product recommendation; e-commerce sites; naive Bayes classification; natural language processing; online customer reviews; online product features; online product reviews; opinion mining; product feature review extraction; product users; review date; review helpfulness score; review polarity; star ratings; Companies; Computers; Data mining; Feature extraction; Mobile handsets; Sentiment analysis; Naïve Bayes Classification; Natural Language Processing; Opinion Mining;
Conference_Titel :
Circuit, Power and Computing Technologies (ICCPCT), 2015 International Conference on
Conference_Location :
Nagercoil
DOI :
10.1109/ICCPCT.2015.7159433
