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A Classification Model for Modeling Online Articles

  • Rula Alhalaseh
  • Ali Rodan
  • Azmi AlazzamEmail author
Conference paper
  • 37 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1187)

Abstract

Due to the constant evolvement of the web and the viral spread of online news on social media, predicting the popularity of a news article became a topic of interest to many categories of people ranging from marketing personnel to politicians. In this paper, we focus on comparing four classification algorithms on a dataset consisting of 39000 news articles taken from Mashable website. The articles were classified into two classes: Popular and not popular. Four different machine learning algorithms were used for classification of the data (KNN, Naïve bayes, Adaboost, and decision tree). Finally, the four classification methods were compared with each other.

Keywords

Ada Boost KNN Naïve Bayes Decision Tree 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information TechnologyUniversity of JordanAmmanJordan
  2. 2.Computer Information ScienceHigher Colleges of TechnologyAl AinUAE

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