Abstract
This work study a poet identification in Arabic poetry using classification methods, with features like poetry features, Sentence length, Characters, word length, first word in the poetry sentence and specific words are used as input data for text classification algorithms: Sequential Minimal Optimization (SMO), Support Vector Machine (SVM), and Linear discriminant analysis (LDA). The data set of experiment divided into two parts: a training dataset with known poets and test dataset with unknown poets. In our experiment, a set of 114 poets from entirely different eras are used. The researcher shows exciting results with a classification accuracy of 98.2456%.
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Acknowledgments
This paper is supported by IBB University in Yemen. The researchers tend to convey thanks to our colleagues from FSTM and INPT who provided insight and skill that greatly motor-assisted the paper.
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Ahmed, A., Mohamed, R., Mostafa, B. (2019). Use SMO SVM, LDA for Poet Identification in Arabic Poetry. In: Khoukhi, F., Bahaj, M., Ezziyyani, M. (eds) Smart Data and Computational Intelligence. AIT2S 2018. Lecture Notes in Networks and Systems, vol 66. Springer, Cham. https://doi.org/10.1007/978-3-030-11914-0_18
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DOI: https://doi.org/10.1007/978-3-030-11914-0_18
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