Automating quranic verses labeling using machine learning approach

Abstract

Classification of Quranic verses into predefined categories is an essential task in Quranic studies. However, in recent times, with the advancement in information technology and machine learning, several classification algorithms have been developed for the purpose of text classification tasks. Automated text classification (ATC) is a well-known technique in machine learning. It is the task of developing models that could be trained to automatically assign to each text instances a known label from a predefined state. In this paper, four conventional ML classifiers: support vector machine (SVM), naïve bayes (NB), decision trees (J48), nearest neighbor (k-NN), are used in classifying selected Quranic verses into three predefined class labels: faith (iman), worship (ibadah), etiquettes (akhlak). The Quranic data comprises of verses in chapter two (al-Baqara) of the holy scripture. In the results, the classifiers achieved above 80% accuracy score with naïve bayes (NB) algorithm recording the overall highest scores of 93.9% accuracy and 0.964 AUC.