This preliminary study explores the potential of Google Artificial Intelligence based Natural Language (GNL-AI) sentiment analysis (SA) as a novel and complementary tool for comparing literary translations at the sentiment level. This study investigates how effectively GNL-AI can assess the emotional fidelity of each translation by analyzing the author's emotional sentiment conveyed in the text. It seeks to move beyond traditional methods often driven by subjectivity or literalness and instead explore a different perspective capable of assessing the emotional fidelity of each translation toward the source text (ST). Ahmed Toufiq's Arabic novel, “Abu Musa’s Women Neighbors”, is the ST that serves as a case study. This research analyzes the sentiment of two translations: English (TT1) by Roger Allen and French (TT2) by Philippe Vigreux. Analyzing the sentiment of 18 ST paragraphs and their corresponding segments in both translations TT1 and TT2 using GNL-AI revealed promising results. Most segments exhibited consistent sentiment patterns, highlighting the potential of this tool for identifying broad emotional trends in literary translations. Three paragraphs showed deviations in sentiment scores, highlighting challenges in achieving perfect alignment between the source text and the translations. Interestingly, the English translation captures well the Arabic author’s intended emotional impact, showcasing the potential of GNL-AI SA in evaluating literary translation quality. In contrast, the French translation tends slightly towards neutrality, highlighting both the promise and the limitations of this approach. Future research will involve expanding the dataset and exploring how GNL-AI can be integrated with human expertise for a more comprehensive analysis.