Text categorisation in Quran and Hadith: Overcoming the interrelation challenges using machine learning and term weighting

Quran and Al-Hadith are interrelated in the sense that both often complement each other in interpreting Islamic teachings. In order to gain comprehension of the Quran in detail, it is vital for a Muslim to refer to Al-Hadith in clarifying ambiguities from the Quran. Al-Hadith offers explanations and lends certainties to the abstract concepts depicted in the Quran. With that, this research proposes a method using text categorisation to classify selected categories by determining the interrelation between the resources. Several interrelated cases were simulated by using a combination of different Islamic resources datasets comprising Quran and Hadiths. The selected three categories; Hajj, Prayer, and Zakat, were compared using three classification methods (Na & iuml;ve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN)) via term weighting; Term Frequency-Inverse Document Frequency (TF-IDF). As a result, the SVM, regardless of being used alone or with term weighting, successfully addressed the interrelationship for single- and multi-label classifications. Additionally, SVM attained better accuracy with 10%-20% improvement, when compared to the other methods that had managed to exhibit slight improvement accuracy wise. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

The Quran and Al-Hadith complement each other in interpreting Islamic teachings. This research proposes a method using text categorisation to classify selected categories and found that Support Vector Machine (SVM) achieved better accuracy in addressing the interrelationship for single- and multi-label classifications.