Use SMO SVM, LDA for Poet Identification in Arabic Poetry

Ahmed, Al-Falahi; Ramdani, Mohamed; Bellafkih, Mostafa

doi:10.1007/978-3-030-11914-0_18

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The best performance average in Table 2 on the techniques level is 89.72% by LDA; it is better than all classifiers techniques in Table 3: • The best performance average in Table 3 for the features level is 98.25% that we obtain after using all attributes together (Ahmed et al, 2019) • The poetry features with other features give a visible indication for identifying the exact author, but while using it alone does not give the best result • The best performance average result is 92.84% by LDA; it is better than the techniques in Table 4 Table 2 The LDA technique has a definite impact on the identification of the real author because of the ability of the technology to handle the features compared to other techniques.…”

Section: Resultsmentioning

confidence: 90%

Arabic Poetry Authorship Attribution using Machine Learning Techniques

Ahmed¹,

Ramdani²,

Bellafkih³

2019

Journal of Computer Science

Self Cite

View full text Add to dashboard Cite

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%.

show abstract

Section: Resultsmentioning

confidence: 90%