Topic Discovery in the Digital Quran: A Text Mining Approach

Abstract

The research addresses the thematic analysis of the Quran, using for this purpose the Surah Al-Kahf and Surah An-Naml via computational approaches. This work outlines the design of a systematic method for understanding the deeply intricate moral, ethical, and spiritual understandings in those chapters. With the help of a Latent Dirichlet Allocation (LDA)-a type of topic modeling algorithm-the current study will extract and then analyze underlying themes from the chosen surahs. It basically involves text filtering, preprocessing, and tokenization before the application of the LDA algorithm. The identified topics were further validated by the Quranic scholars in order to validate their accuracy and theological consistency. Results show the effectiveness of topic modeling in religious text analysis, providing new insights into Quranic themes. This research not only furthers our understanding of the selected surahs but also provides a framework for applying computational techniques to religious text analysis, bridging traditional Islamic studies with modern data science approaches.