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Reseach Article

Product Feature-based Ratings forOpinionSummarization of E-Commerce Feedback Comments

by Vijayshri Ramkrishna Ingale, Rajesh Nandkumar Phursule
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 8
Year of Publication: 2016
Authors: Vijayshri Ramkrishna Ingale, Rajesh Nandkumar Phursule
10.5120/ijca2016908421

Vijayshri Ramkrishna Ingale, Rajesh Nandkumar Phursule . Product Feature-based Ratings forOpinionSummarization of E-Commerce Feedback Comments. International Journal of Computer Applications. 135, 8 ( February 2016), 14-18. DOI=10.5120/ijca2016908421

@article{ 10.5120/ijca2016908421,
author = { Vijayshri Ramkrishna Ingale, Rajesh Nandkumar Phursule },
title = { Product Feature-based Ratings forOpinionSummarization of E-Commerce Feedback Comments },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 135 },
number = { 8 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 14-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume135/number8/24069-2016908421/ },
doi = { 10.5120/ijca2016908421 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:35:48.579152+05:30
%A Vijayshri Ramkrishna Ingale
%A Rajesh Nandkumar Phursule
%T Product Feature-based Ratings forOpinionSummarization of E-Commerce Feedback Comments
%J International Journal of Computer Applications
%@ 0975-8887
%V 135
%N 8
%P 14-18
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the growth of internet, online social networking sites, blogs, discussion forums, etc have gained a tremendous importance. Consumers comment on net to express their views, feedbacks and opinions. The opinion of users is of great importance for mining useful information from the text which can be done through opinion mining techniques. Opinion mining or sentiment analysis is the computational field of study of people’s opinions, emotions, and attitude towards particular Feature. When buying a new product buyer mostly refer the opinion of the other users who have bought the product. Hence, in this work a product Feature rating framework is being proposed. This dissertation comprises mainly of four modules preprocessing, Feature identification, review classification and Feature rating. Finally, the rating are been shown in the graph. For the analysis of the system, we have used Amazon review dataset which consists of customers reviews about product. In the system Apriori algorithm is used for Feature identification, Support Vector Machine algorithm for review classification and SentiWordNet lexicon for giving rating to each Feature of the product.

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Index Terms

Computer Science
Information Sciences

Keywords

Opinion Mining Sentiment Analysis Feature