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