TELKOMNIKA Telecommunication, Computing, Electronics and Control
Comparison analysis of Bangla news articles classification using support vector machine and logistic regression
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Comparison analysis of Bangla news articles classification using support vector machine and logistic regression
Comparison analysis of Bangla news articles classification using support vector machine and logistic regression
Subject
Bangla news
Bangla text classification
Logistic regression
Natural language processing
News classification
Support vector machine
Bangla text classification
Logistic regression
Natural language processing
News classification
Support vector machine
Description
In the information age, Bangla news articles on the internet are fast-growing. For
organizing, every news site has a particular structure and categorization. News arti-
cle classification is a method to determine a document’s classification based on vari-
ous predefined categories. This research discusses the classification of Bangla news
articles on the online platform and tries to make constructive comparison using sev-
eral classification algorithms. For Bangla news articles classification, term frequency-
inverse document frequency (TF-IDF) weighting and count vectorizer have been used
as a feature extraction process, and two common classifiers named support vector ma-
chine (SVM) and logistic regression (LR) employed for classifying the documents. It
is clear that the accuracy of the experimental results by applying SVM is 84.0% and
LR is 81.0% for twelve categories of news articles. In this research work, when we
have made comparison two renowned classification algorithms applied on the Bangla
news articles, LR was outperformed by SVM.
organizing, every news site has a particular structure and categorization. News arti-
cle classification is a method to determine a document’s classification based on vari-
ous predefined categories. This research discusses the classification of Bangla news
articles on the online platform and tries to make constructive comparison using sev-
eral classification algorithms. For Bangla news articles classification, term frequency-
inverse document frequency (TF-IDF) weighting and count vectorizer have been used
as a feature extraction process, and two common classifiers named support vector ma-
chine (SVM) and logistic regression (LR) employed for classifying the documents. It
is clear that the accuracy of the experimental results by applying SVM is 84.0% and
LR is 81.0% for twelve categories of news articles. In this research work, when we
have made comparison two renowned classification algorithms applied on the Bangla
news articles, LR was outperformed by SVM.
Creator
Md Gulzar Hussain, Babe Sultana , Mahmuda Rahman, Md Rashidul Hasan
Source
http://telkomnika.uad.ac.id
Date
Nov 12, 2022
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Md Gulzar Hussain, Babe Sultana , Mahmuda Rahman, Md Rashidul Hasan, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Comparison analysis of Bangla news articles classification using support vector machine and logistic regression,” Repository Horizon University Indonesia, accessed April 26, 2025, https://repository.horizon.ac.id/items/show/4538.
Comparison analysis of Bangla news articles classification using support vector machine and logistic regression,” Repository Horizon University Indonesia, accessed April 26, 2025, https://repository.horizon.ac.id/items/show/4538.