{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:26:44Z","timestamp":1781108804570,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Erciyes University Scientific Research Project Coordination Unit","award":["SBA 202312790"],"award-info":[{"award-number":["SBA 202312790"]}]},{"name":"Erciyes University Scientific Research Project Coordination Unit","award":["SYL-2022-11838"],"award-info":[{"award-number":["SYL-2022-11838"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>Across digital platforms, large-scale assessment of customer sentiment has become integral to brand management, service recovery, and data-driven marketing in e-commerce. Still, most studies center on single-language settings, with bilingual and culturally diverse environments receiving comparatively limited attention. In this study, a bilingual sentiment analysis of consumer feedback on X (formerly Twitter) was conducted for three global quick-service restaurant (QSR) brands\u2014McDonald\u2019s, Burger King, and KFC\u2014using 145,550 English tweets and 15,537 Turkish tweets. After pre-processing and leakage-safe augmentation for low-resource Turkish data, both traditional machine learning models (Na\u00efve Bayes, Support Vector Machines, Logistic Regression, Random Forest) and a transformer-based deep learning model, BERT (Bidirectional Encoder Representations from Transformers), were evaluated. BERT achieved the highest performance (macro-F1 \u2248 0.88 in Turkish; \u22480.39 in temporally split English), while Random Forest emerged as the strongest ML baseline. An apparent discrepancy was observed between pseudo-label agreement (Accuracy &gt; 0.95) and human-label accuracy (EN: 0.75; TR: 0.49), indicating the limitations of lexicon-derived labels and the necessity of human validation. Beyond methodological benchmarking, linguistic contrasts were identified: English tweets were more polarized (positive\/negative), whereas Turkish tweets were overwhelmingly neutral. These differences reflect cultural patterns of online expression and suggest direct managerial implications. The findings indicate that bilingual sentiment analysis yields brand-level insights that can inform strategic and operational decisions.<\/jats:p>","DOI":"10.3390\/jtaer20040294","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T14:00:33Z","timestamp":1762178433000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-Based Sentiment Analysis of E-Commerce Customer Feedback: A Bilingual Parallel Study on the Fast Food Industry in Turkish and English"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8255-1313","authenticated-orcid":false,"given":"Esra","family":"Kahya \u00d6zyirmidokuz","sequence":"first","affiliation":[{"name":"Faculty of Economics and Administrative Sciences, Erciyes University, Kayseri 38030, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0289-3979","authenticated-orcid":false,"given":"Bengisu","family":"Molu Elmas","sequence":"additional","affiliation":[{"name":"Management Information Systems (MIS) Master Program, MIS Department of Graduate Studies, Institute of Social Sciences, Erciyes University, Kayseri 38030, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0693-8433","authenticated-orcid":false,"given":"Eduard Alexandru","family":"Stoica","sequence":"additional","affiliation":[{"name":"Faculty of Economics, Lucian Blaga University, 550324 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1193","DOI":"10.7717\/peerj-cs.1193","article-title":"What People Think about Fast Food: Opinion Analysis and LDA Modeling on Fast Food Restaurants Using Unstructured Tweets","volume":"9","author":"Mujahid","year":"2023","journal-title":"PeerJ Comput. 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