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With people expressing their sentiments in online comments, the Internet has become a great source for \u2018reading\u2019 the zeitgeist via artificial intelligence-based sentiment analysis (SA). At this, negative sentiments are of special concern. They can serve as early indicators for events that require action, such as dropping customer satisfaction, discontent amongst potential voters, or a threat of social unrest. However, due to cultural differences in how negative sentiments are expressed, conventional SA methods typically classify such texts with higher accuracy for some ethnicities compared to others. In this paper, we demonstrate this using a large real-world corpus of Google Maps reviews. Across eight ethnic groups, linguistic patterns vary more starkly in negative (1 star) reviews than in neutral (2\u20134 star) and positive (5 star) reviews. Consequently, ethnicity-blind SA methods \u2018struggle\u2019 to classify negative reviews correctly. To mitigate this problem, we propose a novel SA method, based on balanced training and subsequent ethnicity-conscious fine-tuning. Our approach is simultaneously able to mitigate bias and enhance overall model performance. Thus, we hope to contribute to a more equal appreciation of negative sentiments of ethnically diverse customer-, voter-, or research populations, and, consequently, a more nuanced approximation of the overall zeitgeist.<\/jats:p>","DOI":"10.1007\/s42001-025-00382-y","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T05:20:23Z","timestamp":1747804823000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["\u2018Slightly disappointing\u2019 vs. \u2018worst sh** ever\u2019: tackling cultural differences in negative sentiment expressions in AI-based sentiment analysis"],"prefix":"10.1007","volume":"8","author":[{"given":"Franziska Sofia","family":"Hafner","sequence":"first","affiliation":[]},{"given":"Lena","family":"Hafner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8366-6059","authenticated-orcid":false,"given":"Roberto","family":"Corizzo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,21]]},"reference":[{"key":"382_CR1","unstructured":"ANGLO-EU TRANSLATION GUIDE (2024). 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