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Reseach Article

Ensemble based Hybrid Machine Learning Approach for Sentiment Classification- A Review

by Rabi Narayan Behera, Manan Roy, Sujata Dash
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 6
Year of Publication: 2016
Authors: Rabi Narayan Behera, Manan Roy, Sujata Dash
10.5120/ijca2016910813

Rabi Narayan Behera, Manan Roy, Sujata Dash . Ensemble based Hybrid Machine Learning Approach for Sentiment Classification- A Review. International Journal of Computer Applications. 146, 6 ( Jul 2016), 31-36. DOI=10.5120/ijca2016910813

@article{ 10.5120/ijca2016910813,
author = { Rabi Narayan Behera, Manan Roy, Sujata Dash },
title = { Ensemble based Hybrid Machine Learning Approach for Sentiment Classification- A Review },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 6 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number6/25405-2016910813/ },
doi = { 10.5120/ijca2016910813 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:42.272438+05:30
%A Rabi Narayan Behera
%A Manan Roy
%A Sujata Dash
%T Ensemble based Hybrid Machine Learning Approach for Sentiment Classification- A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 6
%P 31-36
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to digital explosion, huge amount of data is generated from different sources which require critical analysis for decision making. In recent days one of the challenging issues like sentiment classification has drawn the attention of many researchers working in the area of opinion mining. The supervised machine learning technique is used for analyzing sentiments associated with unstructured text data. But, recently it has been observed from the findings that ensemble based learning algorithm achieves better understanding and acceptance of the solution in terms of diversity and accuracy. In this paper, an extensive study of ensemble based machine learning techniques in the domain of sentiment classification has been done to enhance the efficiency, by adopting multiple learning algorithms to obtain better predictive performance, that would be obtained from any of the constituent learning algorithms. Again, how the analysis will become stronger, some suggestions are proposed at the end of the discussion.

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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Ensemble learning Classification Random Subspace Bagging Boosting