News Classification by N-Gram and Machine Learning Algorithms

Authors

  • Department of Computer Science, College of Education for pure Sciences, University of Th-Qar1, Iraq. Imam Ja'afar Al-Sadiq University2, Iraq
  • Department of Computer Science, College of Education for pure Sciences, University of Th-Qar1, Iraq. Imam Ja'afar Al-Sadiq University2, Iraq

DOI:

https://doi.org/10.32792/jeps.v12i2.202

Keywords:

Multinomial Naïve Bayes, Decision Tree, N-gram

Abstract

News is information obtained from different sources such as television, internet, newspapers and
magazines. Online news is published in very large numbers, and because there are so many news, it will
be challenging for users to find the pertinent information that matches their preferences. In this paper, the
news is categorized so that a specific category can be obtained quickly and easily. The BBC's newsgroup
was used in its five categories: sports, politics, business, technology and entertainment. The classification
algorithms Multinomial Naive Bayes (MNB) and decision tree (DT) were applied to the news data set
after extracting the features from it using n-gram method . Multinomial naïve Bayes algorithm has proven
superiority over decision tree with an accuracy 98.2%.

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Published

2023-02-14

Issue

Vol. 12 No. 2 (2022): University of Thi-Qar Journal of education for pure science

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