Abstract
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%.
Highlights
Meter, rhyme, weight and promotion are the most critical elements of ancient Arabic poetry
The concept behind authorship attribution in the case of Arabic poetry lies in the idea that if given a text of a poem as a form of training data from a known poet, it is possible to determine the writer of the unrecognized text in the test data
Alnagdawi et al (2013) developed a program that can identify the meter name of a poem based on Aroud science; this science provides a methodology for classifying Arabic poems into 16 m to assist in locating the meter calls for any Arabic poem using Context-Free Grammar (CFG)
Summary
Rhyme, weight and promotion are the most critical elements of ancient Arabic poetry. Ancient Arabic poetry can be classified into two sets: measured or rhymed and poem prose. The concept behind authorship attribution in the case of Arabic poetry lies in the idea that if given a text of a poem as a form of training data from a known poet, it is possible to determine the writer of the unrecognized text in the test data. It can be done by corresponding the unknown text of the known poet to the potential poet (Al-Falahi et al, 2017). A few related works are available, which make the job easier
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