Sentiment preservation in Quran translation with artificial intelligence approach: study in reputable English translation of the Quran

Abstract This paper addresses the challenge of preserving sentiment when translating sacred texts, with a specific focus on the Quran. The proposed approach combines advanced Artificial Intelligence (AI) techniques, particularly deep learning-based Transformer Language Models (TLMs), with a novel human validation approach. We present a comprehensive study involving a newly created parallel dataset encompassing the Arabic Quran and seven English translations, analyzing the preservation of sentiment. Our findings reveal compelling insights, with neutral sentiment ranging from 59% to 74% in English translations compared to 66% in the original Arabic Quran. Negative sentiment in some translations reached 25%, while others ranged from 14% to 17%, closely paralleling the 24% in the Arabic version. Additionally, the agreement analysis among English translations indicates varying degrees of alignment, reaching a Good level ( κ = 0.62) or a Moderate level ( κ from 0.47 to 0.6). However, compared to the original Arabic Quran, none of the translations achieved high levels of agreement, with only four translations reaching a Fair score (approximately 0.21). These findings underscore the complexities of translating the Quran, particularly its classical Arabic, and emphasize the need for improved sentiment analysis models, potentially incorporating mixed sentiment categories to capture sentiment more effectively.