TOPIC MODELING IN THE HOLY QURAN USING BERTopic

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dc.contributor.authorFatiha, Bouzid
dc.contributor.authorSupervisor: Rahima, Bentercia
dc.date.accessioned2025-07-08T07:20:16Z
dc.date.available2025-07-08T07:20:16Z
dc.date.issued2025-06-15
dc.description.abstractThis research applies topic modeling to the Holy Quran using BERTopic and LaBSE embeddings to automatically extract and classify its main themes. The Qurans thematic complexity requires advanced NLP techniques. The study organizes verses into three categories: Monotheism, Legal Rulings, and Stories. Results show that this AI-based approach supports deeper understanding and thematic analysis of Quranic content.
dc.identifier.urihttps://depot.univ-msila.dz/handle/123456789/46730
dc.language.isoen
dc.publisherMohamed Boudiaf University of M'sila
dc.subjecttopic modeling
dc.subjectHoly Quran
dc.subjectArabic NLP
dc.subjectBERTopic
dc.subjectLaBSE
dc.titleTOPIC MODELING IN THE HOLY QURAN USING BERTopic
dc.typeThesis

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