2021
DOI: 10.1007/978-3-030-85990-9_50
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Sentiment Analysis of Arabic COVID-19 Tweets

Abstract: With the COVID-19 outbreak in 2020, information about the pandemic has been exponentially increasing and spreading across various social media platforms. People across the globe have been affected in a way or another because of different aspects such as the increase in infected cases, death rate increase, financial difficulties, social distancing, being under lockdown, quarantine measures, and working remotely. With people heavily relying on social media platforms to share information more than ever, it is imp… Show more

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Cited by 4 publications
(2 citation statements)
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“…When compared to the other classifiers, the LR produced datasets with a higher accuracy rate. After creating the feature vector in Ahmed D. et al ( 2021 ), using TF-IDF, the authors applied LR, SVM, RF, NB, and K-NN. The best accuracy performance was presented by SVM.…”
Section: Related Workmentioning
confidence: 99%
“…When compared to the other classifiers, the LR produced datasets with a higher accuracy rate. After creating the feature vector in Ahmed D. et al ( 2021 ), using TF-IDF, the authors applied LR, SVM, RF, NB, and K-NN. The best accuracy performance was presented by SVM.…”
Section: Related Workmentioning
confidence: 99%
“…Ahmed et al [12] analyzed Arabic tweets about COVID-19 for sentiments using five different ML models, namely, support vector machine (SVM), Naïve Bayes, random forest, logistic regression and K-Nearest neighbor. They evaluated the five models using Arabic Sentiment Twitter Corpus (ASTC) [13].…”
Section: Related Workmentioning
confidence: 99%