YAZGAN, HARUNÖZBEK, ONURGÜNAY, AHMET CANAKBULUT, FATMA PATLARELİF, YILDIRIMKOCAÇINAR, BÜŞRAŞENGEL, ÖZNUR2023-11-202023-11-202023Yıldırım, E., Yazgan, H., Özbek, O., Günay, A. C., Kocaçınar, B., Şengel, Ö., & Akbulut, F. P. (2023, June). Sentiment Analysis of Tweets on Online Education during COVID-19. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 240-251). Cham: Springer Nature Switzerland.978-303134110-618684238https://doi.org/10.1007/978-3-031-34111-3_21https://hdl.handle.net/11413/8876▪️ Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 675). ▪️ 19th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2023.The global coronavirus disease (COVID-19) pandemic has devastated public health, education, and the economy worldwide. As of December 2022, more than 524 million individuals have been diagnosed with the new coronavirus, and nearly 6 million people have perished as a result of this deadly sickness, according to the World Health Organization. Universities, colleges, and schools are closed to prevent the coronavirus from spreading. Therefore, distance learning became a required method of advancing the educational system in contemporary society. Adjusting to the new educational system was challenging for both students and instructors, which resulted in a variety of complications. People began to spend more time at home; thus, social media usage rose globally throughout the epidemic. On social media channels such as Twitter, people discussed online schooling. Some individuals viewed online schooling as superior, while others viewed it as a failure. This study analyzes the attitudes of individuals toward distance education during the pandemic. Sentiment analysis was performed using natural language processing (NLP) and deep learning methods. Recurrent neural network (RNN) and one-dimensional convolutional neural network (1DCNN)-based network models were used during the experiments to classify neutral, positive, and negative contents.eninfo:eu-repo/semantics/closedAccessCOVID-19Deep LearningDistance EducationSentiment AnalysisSocial MediaconferenceObject2-s2.0-85163360099