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Enhanced Classification Analysis for Product Based Customer Reviews Using Big Data


Author(s):

Shwetha RP , Jerusalem College of Engineering, Pallikaranai, Chennai.; Shelma S, Jerusalem College of Engineering, Pallikaranai, Chennai.; Lilly Sheeba S, Jerusalem College of Engineering, Pallikaranai, Chennai.

Keywords:

Product Reviews, Big Data, Fraudulent Analysis, Customer Segmentation and Product Recommendations

Abstract:

In recent days many web applications tend to collect product reviews from customers to ascertain the satisfaction level on specific products. Big Data analysis can go hand in hand with product reviews in order to bring about useful information that can assist executives and managers in making high end decisions. Big Data analysis not only enables executives to get relevant data in less time but also enables them to carry forward fraudulent analysis, customer segmentation and product recommendations. The proposed system is a web application that takes product reviews from customers as input and performs Enhanced Classification Analysis (ECA) on the reviews using Hadoop to categorize reviews as positive and negative feedbacks. The categorized and segregated positive reviews of products with better comments will be displayed to incoming customers as reports while marketing products. This analysis enables both manufacturers to predict public opinion of their product and also customers to make better decisions and incorporate improved services.


Other Details:

Manuscript Id :IJSTEV2I10021
Published in :Volume : 2, Issue : 10
Publication Date: 01/05/2016
Page(s): 59-61
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