Abstract—This paper presented an authorship attribution in
Arabic poetry using machine learning. Public features in poetry
such as Characters, Poetry Sentence length; Word length,
Rhyme, Meter and First word in the sentence are used as input
data for text mining classification algorithms Naïve Bayes NB
and Support Vector Machine SVM. The main problem: Can we
automatically determine who poet wrote an unknown text, to
solve this problem we use style markers to identify the author.
The dataset of this work was divided into two groups: training
dataset with known Poets and test dataset with unknown Poets.
In this work, a group of 73 poets from completely different eras
are used. The Experiment shows interesting results with
classification precision of 98.63%.
Index Terms—Authorship attribution, Arabic poetry, text
classification, NB, SVM.
Al-Falahi Ahmed is with Computer Science Department in FEN, IBB
University, IBB, Yemen (e-mail: flahi79@gmail.com).
Ramdani Mohamed is with Département d’informatique -FSTM
Université Hassan II Casablanca, Mohammediah, Morocco (e-mail:
moha@fstm.ac.ma).
Bellafkih Mostafa is with Institut National des Postes et
Télécommunications, INPT-Rabat Rabat, Morocco (e-mail:
bellafki@inpt.ac.ma).
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Cite: Al-Falahi Ahmed, Ramdani Mohamed, and Bellafkih Mostafa, "Machine Learning for Authorship Attribution in Arabic Poetry," International Journal of Future Computer and Communication vol. 6, no. 2, pp. 42-46, 2017.
