Lost in Translation? Detecting Emotional Drift in Bilingual Texts of Gilgamesh Using NLP Lost in Translation? Detecting Emotional Drift in Bilingual Texts of Gilgamesh Using NLP
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Abstract
The present work introduces the notion of "emotional drift" in literary translation, which refers to the diversion or transformation of emotional signals from source text to its translations. A bilingual analysis of the Arabic and English translations of The Epic of Gilgamesh serves as the focus of this paper, which, by utilizing recent expansions in Natural Language Processing (NLP) with an emphasis on sentiment analysis and emotion recognition, aims to quantify emotional content. The eventual objectives of this research are to develop a computational pipeline whereby emotional content can be extracted from and compared across bilingual literary corpora, to quantify emotional drift across varying versions of the Gilgamesh, and to contextualize these findings against the larger backdrop of translational research. The intersection of computational linguistics, translation theory, and digital literary analysis opens up a new avenue for machine-assisted literary scholarship and provides a replicable framework for inquiry into the issue of affective fidelity concerning translated literature.
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References
S. Mohammad, Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018: pp. 174–184.
L. Venuti, The translator’s invisibility: A history of translation, Routledge, 2017.
I.A. Ismail, Restoring Gilgamesh Through AI With a Negotiation Algorithm Approach, Theory and Practice in Language Studies 14 (2024) 3046–3056.
E. Vanmassenhove, D. Shterionov, M. Gwilliam, Machine translationese: Effects of algorithmic bias on linguistic complexity in machine translation, ArXiv Preprint ArXiv:2102.00287 (2021).
N.S. Omar, K.A. Kadhim, The Effects of Using AI on the Quality of Translational Product: A Reference to Religious Translation, Al-Noor Journal for Humanities 3 (2025).
J. Feng, Improving sentiment analysis in literary texts through bidirectional encoder representations: A BERT-based approach, Journal of Computational Methods in Sciences and Engineering (2025) 14727978251338004.
E. Kim, R. Klinger, A survey on sentiment and emotion analysis for computational literary studies, ArXiv Preprint ArXiv:1808.03137 (2018).
P. Nandwani, R. Verma, A review on sentiment analysis and emotion detection from text, Soc Netw Anal Min 11 (2021) 81.
R. Nagamanjula, A. Pethalakshmi, A novel framework based on bi-objective optimization and LAN2FIS for Twitter sentiment analysis, Soc Netw Anal Min 10 (2020) 34.
I. Onyenwe, S. Nwagbo, N. Mbeledogu, E. Onyedinma, The impact of political party/candidate on the election results from a sentiment analysis perspective using# AnambraDecides2017 tweets, Soc Netw Anal Min 10 (2020) 55.
E. Ohman, R. Rossi, Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels, ArXiv Preprint ArXiv:2406.01021 (2024).
D. Picca, A. Schnyder, A. Romele, Computational hermeneutics of emotion: a comparative study of emotional landscapes in the Dostoevsky’s novel “Crime and Punishment,” Humanit Soc Sci Commun 11 (2024) 1–10.
Y. Li, M. Afzaal, Y. Yin, A lexicon-based diachronic comparison of emotions and sentiments in literary translation: A case study of five Chinese versions of David Copperfield, PLoS One 19 (2024) e0297101.
R.A. Garcia-Hernández, H. Luna-Garcia, J.M. Celaya-Padilla, A. Garcia-Hernández, L.C. Reveles-Gómez, L.A. Flores-Chaires, J.R. Delgado-Contreras, D. Rondon, K.O. Villalba-Condori, A systematic literature review of modalities, trends, and limitations in emotion recognition, affective computing, and sentiment analysis, Applied Sciences 14 (2024) 7165.