Publication Type:

Journal Article


Advances in Intelligent Systems and Computing, Springer Verlag, Volume 397, p.851-859 (2016)





Consumer reviews, Customer feedback, Feedback, Internet, Market values, Ontology, Opinion words, Parts-of-speech tagging, Probabilistic aspects, ranking algorithm, Rating, Recommender systems, Sales, Soft computing, Stop word, Syntactics


In our day-to-day life we tend to buy products on the Internet. There are plenty of consumer reviews on the Internet. If a customer wants to know about a product, he sees the review and rating of the product given by the product users. In this case we come to know about the importance of rating and review of the product which impacts the product’s market value. This article proposes a framework for calculating an accurate rating using customer feedback. In particular, we first take the consumer review as an input then remove all common words by using the information retrieval concepts like stop word removal and stemming. The next step is parts of speech tagging and finding the opinion word extraction to the rest of the phrases. Then we have to match the keywords with the ontology and finally we develop a probabilistic aspect ranking algorithm to rank the product. We see elaborately about our concept in this article. © Springer India 2016.


cited By 0; Conference of International Conference on Soft Computing Systems, ICSCS 2015 ; Conference Date: 20 April 2015 Through 21 April 2015; Conference Code:160689

Cite this Research Publication

V. Gangothri, Saranya, S., Dr. Venkataraman D., and Panigrahi, B. K., “Engender product ranking and recommendation using customer feedback”, Advances in Intelligent Systems and Computing, vol. 397, pp. 851-859, 2016.