Sentiment Analysis of Tweets on Online Education during COVID-19


YILDIRIM E., Yazgan H., Özbek O., Günay A. C., KOCAÇINAR B., ŞENGEL Ö., ...Daha Fazla

19th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2023, Leon, İspanya, 14 - 17 Haziran 2023, cilt.675 IFIP, ss.240-251 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 675 IFIP
  • Doi Numarası: 10.1007/978-3-031-34111-3_21
  • Basıldığı Şehir: Leon
  • Basıldığı Ülke: İspanya
  • Sayfa Sayıları: ss.240-251
  • Anahtar Kelimeler: COVID-19, Deep Learning, Distance Education, Sentiment Analysis, Social Media
  • İstanbul Kültür Üniversitesi Adresli: Evet

Özet

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.