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International Conference on Applied Computing to Support Industry: Innovation and Technology

ACRIT 2019: Applied Computing to Support Industry: Innovation and Technology pp 79–94Cite as

Classifying Political Arabic Articles Using Support Vector Machine with Different Feature Extraction

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1174))

Abstract

In the recent years, the number of web logs, and the amount of opinionated data on the World Wide Web, have been grown substantially. The ability to determine the political orientation of an article automatically can be beneficial in many areas from academia to security. However, the sentiment classification of web log posts (political web log posts in particular), is apparently more complex than the sentiment classification of conventional text. In this paper, a supervised machine learning with two feature extraction techniques Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) are used for the classification process. For investigation, SVM with four kernels for supervised machine learning have been employed. Subsequent to testing, the results reveal that the linear with TF achieved the results in accuracy of 91.935% also with TF-IDF achieved the 95.161%. The linear kernel was deemed the most suitable for our model.

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Acknowledgments

The authors would like to thank Al-Maarif University College and Dr. Falah Mubark Bardan for supporting this research.

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Authors and Affiliations

  1. Department of Computer Science, University of Technology, Baghdad, Iraq

    Dhafar Hamed Abd, Ahmed T. Sadiq & Ayad R. Abbas

  2. Department of Computer Science, Al-Maarif University College, Alanbar, Iraq

    Dhafar Hamed Abd

Authors
  1. Dhafar Hamed Abd
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  2. Ahmed T. Sadiq
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  3. Ayad R. Abbas
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Corresponding authors

Correspondence to Dhafar Hamed Abd , Ahmed T. Sadiq or Ayad R. Abbas .

Editor information

Editors and Affiliations

  1. Al Maaref University College, Ramadi, Iraq

    Mohammed I. Khalaf

  2. Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily

  3. University of Liverpool, Liverpool, UK

    Alexei Lisitsa

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Abd, D.H., Sadiq, A.T., Abbas, A.R. (2020). Classifying Political Arabic Articles Using Support Vector Machine with Different Feature Extraction. In: Khalaf, M., Al-Jumeily, D., Lisitsa, A. (eds) Applied Computing to Support Industry: Innovation and Technology. ACRIT 2019. Communications in Computer and Information Science, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-030-38752-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-38752-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38751-8

  • Online ISBN: 978-3-030-38752-5

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