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Reseach Article

Polarity Shift Handling Techniques: A Survey

by Daya Mary Mathew, Hema Krishnan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 158 - Number 6
Year of Publication: 2017
Authors: Daya Mary Mathew, Hema Krishnan
10.5120/ijca2017912802

Daya Mary Mathew, Hema Krishnan . Polarity Shift Handling Techniques: A Survey. International Journal of Computer Applications. 158, 6 ( Jan 2017), 1-5. DOI=10.5120/ijca2017912802

@article{ 10.5120/ijca2017912802,
author = { Daya Mary Mathew, Hema Krishnan },
title = { Polarity Shift Handling Techniques: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 158 },
number = { 6 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume158/number6/26909-2017912802/ },
doi = { 10.5120/ijca2017912802 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:04:05.024849+05:30
%A Daya Mary Mathew
%A Hema Krishnan
%T Polarity Shift Handling Techniques: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 158
%N 6
%P 1-5
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a survey on sentiment analysis with respect to the polarity shifting problem. Sentiment Analysis is one of the most widely researched applications of Natural Language Processing. The opinions mostly expressed in social networking sites can be harnessed through automated methods using sentiment analysis. Polarity classification is the most classical sentiment analysis task which aims at classifying reviews into either positive, negative or neutral. Polarity shifting is a challenge to sentiment classification and is considered as one of the main reasons why the standard machine learning algorithms make inaccurate predictions. In this paper various techniques to handle the polarity shift problem are explained. A comparitive study is done on these techniques and the classification performance of each technique is explained.

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Index Terms

Computer Science
Information Sciences

Keywords

Logistic Regression Naive Bayes Polarity Shift Sentiment Classification SVM