Rhythm, Ehsanur RahmanShuvo, Rajvir AhmedHossain, Md SabbirIslam, Md. FarhadulRasel, Annajiat Alim2026-08-222026-08-222023-01-01E. R. Rhythm, R. A. Shuvo, M. S. Hossain, M. F. Islam and A. A. Rasel, "Sentiment Analysis of Restaurant Reviews from Bangladeshi Food Delivery Apps," 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2023, pp. 1-5, doi: 10.1109/ESCI56872.2023.10100214.97816654752422-s2.0-85158128025https://hdl.handle.net/10361/29416In this study, we conducted sentiment analysis on restaurant reviews from Bangladeshi food delivery apps using natural language processing techniques. Food delivery apps have become increasingly popular in Bangladesh, and understanding the sentiment of customer reviews can provide valuable insights for restaurant owners and food delivery app companies. In this research, we have created a dataset named 'Bangladeshi Restaurant Reviews' by gathering customer reviews of restau-rants available on Foodpanda and Hungrynaki, which are two popular food delivery apps in Bangladesh. We used Robustly Optimized BERT Pretraining Approach (RoBERTa), AFINN, and DistilBERT, a distilled version of Bidirectional Encoder Repre-sentations from Transformers (BERT) to perform the sentiment analysis. Overall, this research paper highlights the importance of sentiment analysis in the food delivery industry and demonstrates the effectiveness of different models in performing this task. It also provides insights for businesses looking to use sentiment analysis to improve their services and products. The accuracy of the models evaluated, RoBERTa, AFINN, and DistilBERT, were 74%, 73 %, and 77 % respectively.5 Pagesen-USSentiment analysisAnalytical modelsComputational modelingBit error ratePipelinesTransformersRestaurant ReviewsFood DeliveryText analysisRoBERTaDistilBERTSentiment analysis.Natural language processing (Computer science).Conference Proceeding10.1109/ESCI56872.2023.10100214