Feedbuzz : A feedback system with Automated solutions
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Volume & Issue no: Volume 5, Issue 5, September - October 2016

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Title:
Feedbuzz : A feedback system with Automated solutions
Author Name:
Pratik Daine, Monica Masne, Abhishek Bawage , Prof.Sandeep Gore
Abstract:
Abstract The currently present system take feedback from customers for their respective products in unorganized manner i.e. negative and positive feedbacks aren’t sorted. This makes it difficult for the administration to have a clear picture of a specific product. Also admin has to manually provide solutions to the customers in case of any inconsistencies. The main motivation of our project is to overcome the above mentioned problem. This system will segregate the positive and negative feedbacks given by the customers. Simultaneously it will automatically generate solutions to the negative feedbacks. Categorization of the negative and positive feedbacks will help the administrator to have a clear picture of the complete performance of the product. Our system is mainly based on Apriori and MOPNAR (Multi Objective Positive Negative Association Rules) algorithms. Using Apriori set of rules, association rules are generated and using MOPNAR (Multi Objective Positive Negative Association Rule) algorithm, solutions are provided to negative reviews. Keywords: feedback; mopnar; sentimental analysis; apriori; segregation; association rules; data mining
Cite this article:
Pratik Daine, Monica Masne, Abhishek Bawage , Prof.Sandeep Gore , " Feedbuzz : A feedback system with Automated solutions " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 5, Issue 5, September - October 2016 , pp. 053-056 , ISSN 2278-6856.
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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
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