A Computational Approach for Identifying Quranic Themes

In this paper, we present a simple mining technique named the Quran Mining Technique (QMT) in an attempt to automatically classify the Suras (i.e. chapters) of the Quran based on predefined set of 10 themes. QMT is composed mainly of two phases: a preprocessing phase and a classification phase. In the first phase, we manually label a set of representative words for ten predefined themes. In the second phase we use the QMT on a set of 14 Suras (the total number of Suras is 30) using a scoring function (SF) to identify their themes. The results of QMT are compared with the results obtained from expert scholars in the field of Quranic studies, which we used as a benchmark. The average accuracy of the QMT classifier shows a result close to 79%.