Research Article
Multi-Perspective Analysis of News Articles using Machine Learning Algorithms
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 181 - Issue 42 |
| Published: Feb 2019 |
| Authors: Vaibhav Khatavkar, Makarand Velankar, Parag Kulkarni And |
10.5120/ijca2019918503
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Vaibhav Khatavkar, Makarand Velankar, Parag Kulkarni And . Multi-Perspective Analysis of News Articles using Machine Learning Algorithms. International Journal of Computer Applications. 181, 42 (Feb 2019), 22-26. DOI=10.5120/ijca2019918503
@article{ 10.5120/ijca2019918503,
author = { Vaibhav Khatavkar,Makarand Velankar,Parag Kulkarni And },
title = { Multi-Perspective Analysis of News Articles using Machine Learning Algorithms },
journal = { International Journal of Computer Applications },
year = { 2019 },
volume = { 181 },
number = { 42 },
pages = { 22-26 },
doi = { 10.5120/ijca2019918503 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2019
%A Vaibhav Khatavkar
%A Makarand Velankar
%A Parag Kulkarni And
%T Multi-Perspective Analysis of News Articles using Machine Learning Algorithms%T
%J International Journal of Computer Applications
%V 181
%N 42
%P 22-26
%R 10.5120/ijca2019918503
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Nowadays, many machine learning algorithms are evolving. It is a very difficult task to select a particular algorithm for a specific problem. A multi-perspective analysis of the given input data has to be performed to select a particular algorithm. In this study a case study has been taken for selecting an algorithm for the classification of news articles. Multi-perspective analysis is performed on the data using various machine learning algorithms namely Random Forest Classifier, Decision tree, AdaBoostClassifier, SVM with Linear SVC and SVM with NuSVC. For the multi perspective analysis, features from the dataset are extracted and standard metrics are used. The metrics used are Kappa, Accuracy, F-measure, Recall, and Precision. For the BBC news standard dataset, SVM Linear SVC proves to be effective because its classification rate is 96% and false positive rate is 0.75%.
References
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