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COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

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submitted on 2024-09-01, 06:32 and posted on 2024-09-01, 07:04 authored by Syed Ali Jafar Zaidi, Indranath Chatterjee, Samir Brahim Belhaouari

In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.

Other Information

Published in: Applied Computational Intelligence and Soft Computing
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1155/2022/1209172

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Hindawi

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU

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