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Adaptive Product Review Classification Engine (APRCE) for Social Network Product Review Evaluation

by Anjani Chanji, Rekha Bhatia
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 7
Year of Publication: 2016
Authors: Anjani Chanji, Rekha Bhatia
10.5120/ijca2016910889

Anjani Chanji, Rekha Bhatia . Adaptive Product Review Classification Engine (APRCE) for Social Network Product Review Evaluation. International Journal of Computer Applications. 146, 7 ( Jul 2016), 41-45. DOI=10.5120/ijca2016910889

@article{ 10.5120/ijca2016910889,
author = { Anjani Chanji, Rekha Bhatia },
title = { Adaptive Product Review Classification Engine (APRCE) for Social Network Product Review Evaluation },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 7 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number7/25414-2016910889/ },
doi = { 10.5120/ijca2016910889 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:49.370785+05:30
%A Anjani Chanji
%A Rekha Bhatia
%T Adaptive Product Review Classification Engine (APRCE) for Social Network Product Review Evaluation
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 7
%P 41-45
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The product review classification is the process of automatic categorization of the review data based upon the specific products. The product review classification helps the techniques for analysis of the product reviews and to classify them according to the polarity evaluation. The product review classification has been utilized for the comparative analysis of the two similar products to facilitate the customers to make their decision on the basis of the public opinion and choice. In this paper, the product review classification model has been implemented with the supervised modeling, which evaluates the multiple keyword lists for the evaluation of the polarity across the input review data for the different products of the similar categories. The proposed model performance has been evaluated in thevarious domains such as text processing errors, compression types, recall, precision, polarization accuracy, etc. The proposed model has been found efficient in the terms of all of the performance parameters in comparison with the existing models.

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Index Terms

Computer Science
Information Sciences

Keywords

Product reviews classification Polarization Sentence Compression Review Classification.

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