doi: 10.18178/ijfcc.2017.6.2.486
Machine Learning for Authorship Attribution in Arabic Poetry
Al-Falahi Ahmed
1,3
,
Ramdani Mohamed
2
,
Bellafkih Mostafa
3
- 1 Computer Science Department in FEN, IBB University, IBB, Yemen.
- 2 Département d’informatique -FSTM Université Hassan II Casablanca, Mohammediah, Morocco.
- 3 Institut National des Postes et Télécommunications, INPT-Rabat Rabat, Morocco.
Abstract
This paper presented an authorship attribution inArabic poetry using machine learning. Public features in poetrysuch as Characters, Poetry Sentence length; Word length,Rhyme, Meter and First word in the sentence are used as inputdata for text mining classification algorithms Naïve Bayes NBand Support Vector Machine SVM. The main problem: Can weautomatically determine who poet wrote an unknown text, tosolve this problem we use style markers to identify the author.The dataset of this work was divided into two groups: trainingdataset with known Poets and test dataset with unknown Poets.In this work, a group of 73 poets from completely different erasare used. The Experiment shows interesting results withclassification precision of 98.63%.
Keywords
- Authorship attribution
- Arabic poetry
- textclassification
- NB
- SVM
How to 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. https://doi.org/ 10.18178/ijfcc.2017.6.2.486
Copyright & License
Copyright © 2017 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).