Abstract: Stylometric Authorship attribution is one of the essential approaches in the text mining. The present research endorses a Stylometric method called Stylometric Authorship Ranking Attribution (SARA) overcomes the usual problems which are processing time and accurate prediction results, without any human opinion that relays on the domain expert. This new method also uses the most effective attributes used in the Stylometric authorship prediction frequent word bag counts, whether it was frequent single, pair or trio words attributes, which are the most successful attributes in Stylometric prediction, having more alibi for author artistic writing style for our authorship recognition and prediction proposed technique. The experiments show that the proposed method produces superior prediction accuracy and even provides a completely correct result at the final stage of our experimental tests regarding the dataset scope.

Marwa Taha Jamil and Dr. Tareef kamil Mustafa, “Ranking Attribution: A Novel Method for Stylometric Authorship Identification” International Journal of Advanced Computer Science and Applications(IJACSA), 9(7), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090709

@article{Jamil2018,
title = {Ranking Attribution: A Novel Method for Stylometric Authorship Identification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090709},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090709},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {7},
author = {Marwa Taha Jamil and Dr. Tareef kamil Mustafa}
}