Understating Factors Affecting Traveling During COVID-19 Using Sentiment Analysis

Mostafa, Lamiaa; Beshir, Sara

doi:10.1007/978-3-030-89701-7_10

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…In Equation (3), w is a weights vector, x is the input vector for training, and b is the bias. Equation (4) represents the optimization problem in the linear support vector machines classifier [ 39 ]:

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Section: Methodsmentioning

confidence: 99%

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Carvalho

Nepomuceno²,

Poleto³

et al. 2022

TropicalMed

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This article presents a study that applied opinion analysis about COVID-19 immunization in Brazil. An initial set of 143,615 tweets was collected containing 49,477 pro- and 44,643 anti-vaccination and 49,495 neutral posts. Supervised classifiers (multinomial naïve Bayes, logistic regression, linear support vector machines, random forests, adaptative boosting, and multilayer perceptron) were tested, and multinomial naïve Bayes, which had the best trade-off between overfitting and correctness, was selected to classify a second set containing 221,884 unclassified tweets. A timeline with the classified tweets was constructed, helping to identify dates with peaks in each polarity and search for events that may have caused the peaks, providing methodological assistance in combating sources of misinformation linked to the spread of anti-vaccination opinion.

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Section: Methodsmentioning

confidence: 99%