Quantifying Love: A Data Analysis of the Many Names of Love in Arabic Poetry (quantifiedcuriosities.com)

A recent data-driven study delves into the rich lexical complexity of love in Arabic poetry by analyzing 14 distinct Arabic words for love across twelve historical eras using a 2-million-verse corpus combined with sentiment analysis. This work reveals how poets strategically use nuanced synonyms to convey different emotional tones, intensities, and psychological stages of love, from initial attraction to obsessive madness and self-sacrifice. The research highlights the semantic field of love as a spectrum with subtle gradations, demonstrating that each term carries specific connotations rooted in etymology that go beyond mere synonyms—some even encompass elements of physical or emotional harm. For the AI/ML community, this project is significant as it combines natural language processing with cultural linguistics to uncover deep semantic relationships in classical texts. The sentiment analysis and word co-occurrence mappings showcase how AI can enrich digital humanities by quantifying poetic nuance and emotional polarity in a language noted for its lexical diversity. The publicly available dataset and code offer valuable resources for further exploration into embedding cultural context and historical linguistics into language models, enhancing their sensitivity to semantic subtleties often lost in machine translation or broad text analysis. This represents a promising interdisciplinary bridge between AI, linguistics, and literary studies.