Research Article Open Access

Arabic Poetry Authorship Attribution using Machine Learning Techniques

Al-Falahi Ahmed1, Ramdani Mohamed2 and Bellafkih Mostafa3
  • 1 IBB University, Yemen
  • 2 FSTM Université Hassan II, Morocco
  • 3 Institut National des Postes et Télécommunications, Morocco


In this study, authorship attribution in Arabic poetry will be conducted to determine the authorship of a specified text after documents with recognized authorships have been allocated. This work also measures the impact performance of Naïve Bayes, Support Vector Machine and Linear discriminant analysis for Arabic poetry authorship attribution using text mining classification. Several features such as lexical features, character features, structural features, poetry features, syntactic features, semantic features and specific word features are utilized as the input data for text mining, using classification algorithms Linear discriminant analysis, Support Vector Machine and Naïve Bayes by Arabic Poetry Authorship Attribution Model (APAAM). The dataset of Arabic poetry is divided into two sets: known poetic in training dataset texts and anonymous poetic texts in a test dataset part. In the experiment, a set of 114 random poets from entirely different eras are used. The highest performance accuracy value is 99, 12%; the performance rate at the attribute level is 98.246%; the level of techniques is 92.836%.

Journal of Computer Science
Volume 15 No. 7, 2019, 1012-1021


Submitted On: 18 June 2019 Published On: 27 July 2019

How to Cite: Ahmed, A., Mohamed, R. & Mostafa, B. (2019). Arabic Poetry Authorship Attribution using Machine Learning Techniques. Journal of Computer Science, 15(7), 1012-1021.

  • 8 Citations



  • Authorship Attribution
  • Arabic Poetry
  • Text Classification
  • Machine Learning Techniques