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Product Aspect Ranking and Fraud Detection

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IJCA Proceedings on National Conference on Advances in Computing, Communication and Networking
© 2016 by IJCA Journal
ACCNET 2016 - Number 2
Year of Publication: 2016
Authors:
Pradnya Zende
Rakhi Satpute
Poonam Panchal
Sandeep Gore

Pradnya Zende, Rakhi Satpute, Poonam Panchal and Sandeep Gore. Article: Product Aspect Ranking and Fraud Detection. IJCA Proceedings on National Conference on Advances in Computing, Communication and Networking ACCNET 2016(2):28-31, June 2016. Full text available. BibTeX

@article{key:article,
	author = {Pradnya Zende and Rakhi Satpute and Poonam Panchal and Sandeep Gore},
	title = {Article: Product Aspect Ranking and Fraud Detection},
	journal = {IJCA Proceedings on National Conference on Advances in Computing, Communication and Networking},
	year = {2016},
	volume = {ACCNET 2016},
	number = {2},
	pages = {28-31},
	month = {June},
	note = {Full text available}
}

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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