Advancing Literary Analysis With Python: A Comprehensive Study of Simile Detection and Classification in the Translation of Al-Abrat

This study explores the intricate use of similes within Al-’Abrat (“The Tears”) by Mustafa Lutfi al-Manfaluti, a cornerstone of modern Arabic literature. The research employs Abdul-Raof’s classification framework, combining traditional Arabic rhetorical theory with computational methods, including Python-based algorithms, to detect and categorize similes. By analyzing 212 similes extracted from the English translation “The Tears” by Majid Khan Malik Saddiqui, the study highlights similes’ cognitive and rhetorical functions in enriching emotional resonance and cultural depth. Key findings reveal distinct patterns in simile usage, particularly the prevalence of perceptible-perceptible and cognitive-cognitive categories, emphasizing the role of figurative language in fostering thematic and emotional engagement. The interdisciplinary approach bridges Arabic literary criticism, cognitive linguistics, and natural language processing (NLP), offering methodological innovations for simile detection and classification. While effective, the study acknowledges limitations, including reliance on a single corpus, subjectivity in manual validation, and the need for advanced machine learning models for nuanced analysis. This research contributes to enhancing the interpretive framework for Arabic rhetoric and broadening the application of computational tools in literary studies. Future directions propose expanding the corpus, integrating diverse rhetorical devices, and employing sophisticated NLP techniques to further uncover the richness of Arabic literary heritage.