International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume4,March 2017,

Topic : Methods to Improve Genuine Ratings of Products in E-Commerce Sites

Authors:P.M.D.Ali Khan || K.Manoj Kumar

Abstract:Various customer surveys of item square measure as of now offered on the web. Customer surveys contain well off and profitable learning for both firms and clients. Nonetheless, the audits are frequently confused, bringing about challenges in information route and learning securing. This article proposes an item feature positioning structure, which naturally distinguishes the imperative parts of item from on-line customer surveys, going for raising the ease of use of the different audits. The imperative item angles square measure known bolstered 2 perceptions: 1) the key viewpoints square measure commonly remarked on by an outsized scope of shoppers and 2) customer conclusions on the essential perspectives enormously impact their general suppositions on the stock. Especially, given the purchaser audits of an item, we tend to first set up item angles by a shallow reliance program and check customer feelings on these viewpoints by means of a notion classifier. We tend to then build up a probabilistic feature positioning principle to induce the significance of aspects by in the meantime considering aspect recurrence and along these lines the impact of customer sentiments given to each angle over their general suppositions. The trial comes about on an audit corpus of twenty one stylish items in eight spaces exhibit the adequacy of the anticipated approach. Additionally, we have a tendency to apply item feature positioning to 2 certifiable applications, i.e., archive level assessment characterization and extractive survey account, and finish imperative execution upgrades, that show the limit of item aspect positioning in encouraging true applications..

Keywords: classification, identification, ranking, fake reviews.

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DOI: 01.1617/vol4/iss3/pid84067

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Dr. Allon Guez
Professor, Drexel University,

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
Frequency : monthly
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