Abstract:

Text classification, also known as text tagging, is the process of dividing a given text into organized groups. Using Natural Language Processing methods, text classifiers can automatically analyze text and then assign a set of predefined tags or categories based on its content. If it is a verse of the Holy Qur'an, the main purpose of labeling is to determine the theme of the verse. However, current approaches to verse tagging depend primarily on the availability of scholars with deep expertise in the Arabic language and Qur'anic exegesis. In this study, it is suggested to automate the task of tagging Qur'anic verses using text classification algorithms. In the experiments we carried out with the classification algorithms, the 15 predefined categories to which the English translations of the verses belong were used as features. Unlike similar studies in the literature, Genetic Algorithm was used in the feature selection stage. Thus, it is aimed that this intermediate step will have a positive effect on the final performance. At the end of the study, the performance values of the classification models are given comparatively by using various performance evaluation metrics.

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