Author:
Hamza Manar Ahmed,Hashim Aisha Hassan Abdalla,Motwakel Abdelwahed,Elhameed Elmouez Samir Abd,Osman Mohammed,Kumar Arun,Singla Chinu,Munjal Muskaan
Funder
Prince Sattam bin Abdulaziz University
Publisher
Springer Science and Business Media LLC
Reference26 articles.
1. Ainapure BS, Pise RN, Reddy P, Appasani B, Srinivasulu A, Khan MS, Bizon N. Sentiment analysis of COVID-19 tweets using deep learning and lexicon-based approaches. Sustainability. 2023;15:2573.
2. Fattoh IE, Kamal Alsheref F, Ead WM, Youssef AM. Semantic sentiment classification for COVID-19 tweets using universal sentence encoder. Comput Intell Neurosci. 2022;2022:6354543.
3. Stitini O, Twil A, Kaloun S, Bencharef O. How can we analyse emotions on Twitter during an epidemic situation? A features engineering approach to evaluate people’s emotions during the COVID-19 pandemic. J Tianjin Univ Sci Technol. 2021;54.
4. Sitaula C, Basnet A, Mainali A, Shahi TB. Deep learning-based methods for sentiment analysis on Nepali COVID-19-related tweets. Comput Intell Neurosci. 2021;2021:2158184.
5. Anuradha K, Parvathy M. Multi-label emotion classification of COVID-19 tweets with deep learning and topic modelling. Comput Syst Sci Eng. 2023;46:3005–21.