Exploring Thematic Structures in Surah Al-Baqarah using TF-IDF, Dimensionality Reduction, and K-means Clustering | ||||
| Journal of Computing and Communication | ||||
| Article 1, Volume 4, Issue 2, July 2025, Page 1-12 PDF (1.47 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2025.446634 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Ahmad Salah1; Mahmoud Mahdi1; amro ali badawy2 | ||||
| 1Faculty of Computers and Informatics, Zagazig University | ||||
| 2Computer science ,faculity of computers and informatics ,zagazig univercity | ||||
| Abstract | ||||
| This research work delineates a computational investigation into the thematic structures of all verses from Surah Al-Baqarah usingunsupervised semantic clustering. Given the unique character of Quranic Arabic, the data was pre-processed in a manner aligning with the specific text, starting with diacritic removal and utilizing standard Arabic and customized Quranic stopword lists. The verses were vectorized utilizing the Term Frequency-Inverse Document frequency (TF-IDF) and as such, representing the verses semantic content. Following vectorization, dimensionality reduction was then conducted using Truncated Singular Value Decomposition (SVD) along with Uniform Manifold Approximation and Projection (UMAP), primarily for purposes of visualization and then clustering. Next, K-means clustering was employed to segment the resulting UMAP embeddings into groupings of semantically similar verses. The quality of the clustering solutions ranging from (k=2 to 10) clusters were assessed utilizing three standard clustering metrics: Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. Inclusively, from the data of these metrics a 10-cluster solution was identified as the optimal solution for this data utilizing a weighted average of each index using combination values of one third among the three metrics. The visualizations using UMAP project the verses into a 2D space and show how the different clustering configurations of 3, 5, 7 and 10 clusters provide a distinct separation in the thematic groups identified by the K-means algorithm. The 10-cluster solution was interpreted by examining the top TF-IDF terms and examples of verses in each cluster and assigned themes of 'Faith and Belief', 'Divine Law and Guidance', 'Family Relations', and 'Moral Conduct' as examples of themes observed in each group. This study illustrates the use of a TF-IDF, SVD, UMAP and K-means pipeline to quantitatively examine and visualize a representation of theme organization of the semantic structures of verses in Surah Al-Baqarah. | ||||
| Keywords | ||||
| Quranic studies; topic modeling; clustering; digital humanities; natural language processing; sentiment analysis | ||||
| References | ||||
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