Gender Prediction of Journalists from Writing Style

  • Peshawa J. Muhammad Ali Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
  • Nigar M. Shafiq Surameery Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
  • Abdul-Rahman Mawlood Yunis Canada Revenue Agency, Ottawa, Ontario,
  • Ladeh Sardar Abdulrahman Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
DOI: https://doi.org/10.14500/aro.10031
Keywords: Gender identification, Kurdish media, Neural networks, Text mining

Abstract

Web-based Kurdish media have seen a tangible growth in the last few years. There are many factors that have contributed into this rapid growth. These include an easy access to the internet connection, the low price of electronic gadgets and pervasive usage of social networking. The swift development of the Kurdish web-based media imposes new challenges that need to be addressed. For example, a newspaper article published online possesses properties such as author name, gender, age, and nationality among others. Determining one or more of these properties, when ambiguity arises, using computers is an important open research area. In this study the journalist’s gender in web-based Kurdish media determined using computational linguistic and text mining techniques. 75 web-based Kurdish articles used to train artificial model designed to determine the gender of journalists in web-based Kurdish media. Articles were downloaded from four different well known web-based Kurdish newspapers. 61 features were extracted from each article; these features are distinct in discriminating between genders. The Multi-Layer Perceptron (MLP) artificial neural network is used as a classification technique and the accuracy received were 76%.

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Author Biographies

Peshawa J. Muhammad Ali, Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
Peshawa J. Muhammad Ali is a lecturer and researcher at the Department of Software Engineering, Koya University since 2006. He has B.Sc. in Civil Engineering and M.Sc. in Computer Science. His main research area is data mining and machine learning with several published articles in the field of neural networks. Mr. Peshawa is a Consultant Civil Engineer at the Kurdistan Engineering Union and he has an experience in this field.
Nigar M. Shafiq Surameery, Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
Department of Software Engineering, Faculty of Engineering.
Ladeh Sardar Abdulrahman, Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
Department of Software Engineering, Faculty of Engineering.

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ARO Journal: Volume 1, No. 1 (2013)
Published
2013-11-20
How to Cite
Muhammad Ali, P. J., Shafiq Surameery, N. M., Yunis, A.-R. M. and Abdulrahman, L. S. (2013) “Gender Prediction of Journalists from Writing Style”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 1(1), pp. 22-28. doi: 10.14500/aro.10031.
Issue
Vol. 1 No. 1 (2013): Issue One
Section
Articles

Copyright (c) 2013 Peshawa J. Muhammad Ali, Nigar M. Shafiq Surameery, Abdul-Rahman Mawlood Yunis, Ladeh Sardar Abdulrahman

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