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Reseach Article

Product Aspect Ranking and Fraud Detection

Published on June 2016 by Pradnya Zende, Rakhi Satpute, Poonam Panchal, Sandeep Gore
National Conference on Advances in Computing, Communication and Networking
Foundation of Computer Science USA
ACCNET2016 - Number 2
June 2016
Authors: Pradnya Zende, Rakhi Satpute, Poonam Panchal, Sandeep Gore
8c5f7dc3-e495-46d6-a280-33d76e52c8bc

Pradnya Zende, Rakhi Satpute, Poonam Panchal, Sandeep Gore . Product Aspect Ranking and Fraud Detection. National Conference on Advances in Computing, Communication and Networking. ACCNET2016, 2 (June 2016), 28-31.

@article{
author = { Pradnya Zende, Rakhi Satpute, Poonam Panchal, Sandeep Gore },
title = { Product Aspect Ranking and Fraud Detection },
journal = { National Conference on Advances in Computing, Communication and Networking },
issue_date = { June 2016 },
volume = { ACCNET2016 },
number = { 2 },
month = { June },
year = { 2016 },
issn = 0975-8887,
pages = { 28-31 },
numpages = 4,
url = { /proceedings/accnet2016/number2/24980-2269/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computing, Communication and Networking
%A Pradnya Zende
%A Rakhi Satpute
%A Poonam Panchal
%A Sandeep Gore
%T Product Aspect Ranking and Fraud Detection
%J National Conference on Advances in Computing, Communication and Networking
%@ 0975-8887
%V ACCNET2016
%N 2
%P 28-31
%D 2016
%I International Journal of Computer Applications
Abstract

In this paper we are going to find important aspect of the product and its rank this aspect by using numerous consumer reviews. The consumer reviews contain a rich and an important knowledge about the product. This knowledge is also useful for both consumer and firms. Consumers can make wise purchasing decision by the paying more attention towards important aspect or feature. And firm will be concentrate on important features or aspect while improving the quality of the aspect. In this proposed framework, identify an important aspect of product from online consumer reviews. The consumer reviews an important aspect are identified by using the one tool which is nothing but the NPL tool, and it will also classify the sentiment on that aspect, and finally we are going to apply the ranking framework algorithm to determine the particular product rating. We are using shallow dependency parser to identify product aspect ranking. In this framework for identify aspects use sentiment classification method. The extractive review summarization and document-level sentiment classification use for product aspect ranking. This ranking are done based on usually commented review and consumer opinion about the product.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Classification Document Level Sentiment Classification Extract Review Summarization.