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Improve Sentiment Analysis Accuracy using Multiple Kernel Approach

by Ruchika Sharma, Amit Arora
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 20
Year of Publication: 2013
Authors: Ruchika Sharma, Amit Arora
10.5120/12601-9388

Ruchika Sharma, Amit Arora . Improve Sentiment Analysis Accuracy using Multiple Kernel Approach. International Journal of Computer Applications. 71, 20 ( June 2013), 12-15. DOI=10.5120/12601-9388

@article{ 10.5120/12601-9388,
author = { Ruchika Sharma, Amit Arora },
title = { Improve Sentiment Analysis Accuracy using Multiple Kernel Approach },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 20 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number20/12601-9388/ },
doi = { 10.5120/12601-9388 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:36:07.875609+05:30
%A Ruchika Sharma
%A Amit Arora
%T Improve Sentiment Analysis Accuracy using Multiple Kernel Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 20
%P 12-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment Analysis has become an indispensible part of product reviews in present scenario. Sentiment Analysis is a very well studied field, but the scale remains limited to not more than a few hundred researchers. The problem of analyzing the overall sentiment of a document using Machine learning techniques has been considered. Results have been improved using multiple kernel approach and compared with previously used techniques. . The present research is a comparison and extension of the work proposed by Mullen and Collier (2003). The system consists of a feature Extraction phase and a learning phase; on the basis of which the overall sentiment of the document is analyzed. The present work uses the movie review data set used by Pang (2002). The approach significantly outperforms the previous methods attaining 90% and 92% accuracy using 5 fold cross validation 10 fold cross validation respectively.

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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Feature Extraction SVM PCA Kernel Multiple kernel

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