Publication Type:

Conference Paper

Source:

Circuit, Power and Computing Technologies (ICCPCT), 2015 International Conference on (2015)

Keywords:

Bayes methods, buyer opinion, Companies, Computers, consumer behaviour, customer product recommendation, Data mining, e-commerce sites, Electronic commerce, Feature extraction, Mobile handsets, naive Bayes classification, NAtural language processing, online customer reviews, online product features, online product reviews, opinion mining, Pattern classification, product feature review extraction, product users, Recommender systems, retail data processing, review date, review helpfulness score, review polarity, Sentiment analysis, star ratings, Web based system, Web sites

Abstract:

<p>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.</p>

Cite this Research Publication

P. V. Rajeev and V Smrithi Rekha, “Recommending products to customers using opinion mining of online product reviews and features”, in Circuit, Power and Computing Technologies (ICCPCT), 2015 International Conference on, 2015.