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Inforank an approach for review ranking

Author:

Akshit Bhatia, Kanika Mittal, Natansh Negi and Vaibhav Tomar

Subject Area:

Physical Sciences and Engineering

Abstract:

Reading numerous reviews about a product often is a cumbersome work. Many times, the review only say that the product is good and nothing else about the design, functionality of that product and hence reading those types of reviews doesn’t influence the decision of the customers. So to make the life of a customer easier, this paper presents an algorithm that ranks the reviews on the basis of the amount of information in the review. The earlier works done on the ranking of reviews assumed that all features were equally important and hence the ranking was never based on the amount of information that can be extracted from the review. This algorithm is an improvement over the other works in the similar domain as it extracts the features based on their importance among the users and then ranks the review as per the information available in the review.

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