An Exhaustive Sentiment and Emotion Analysis of COVID-19 Tweets Using Machine Learning, Ensemble Learning and Deep Learning Techniques
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-9888-1_36
Reference21 articles.
1. Hu Y, Sun J, Dai Z, Deng H, Li X, Huang Q, Wu Y, Sun L, Xu Y (2020) Prevalence and severity of corona virus disease 2019 (COVID-19): a systematic review and meta-analysis. J Clin Virol 127:104371. https://doi.org/10.1016/j.jcv.2020.104371
2. Chu DK, Akl EA, Duda S, Solo K, Yaacoub S, Schünemann HJ (2020) Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet 395:1973–1987. https://doi.org/10.1016/S0140-6736(20)31142-9
3. Adikari A, Nawaratne R, De Silva D, Ranasinghe S, Alahakoon O, Alahakoon D (2021) Emotions of COVID-19: content analysis of self-reported information using artificial intelligence. J Med Internet Res 23(4):e27341. https://doi.org/10.2196/27341
4. Machucal C, Cristian GC, Renato M, Toasa R (2020) Twitter sentiment analysis on coronavirus: machine learning approach. In: Proceedings of international symposium on automation, information and computing (ISAIC 2020), vol 1828
5. Dataset accessed from https: //www.kaggle.com/c/sentiment-analysis-of-covid-19-related-tweets/overview in April 2022
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