COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers
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, soci...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2022/1209172 |
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| _version_ | 1849402321107156992 |
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| author | Syed Ali Jafar Zaidi Indranath Chatterjee Samir Brahim Belhaouari |
| author_facet | Syed Ali Jafar Zaidi Indranath Chatterjee Samir Brahim Belhaouari |
| author_sort | Syed Ali Jafar Zaidi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8adfea2ef83847309a59ed8e1b913dc3 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-8adfea2ef83847309a59ed8e1b913dc32025-08-20T03:37:33ZengWileyApplied Computational Intelligence and Soft Computing1687-97322022-01-01202210.1155/2022/1209172COVID-19 Tweets Classification during Lockdown Period Using Machine Learning ClassifiersSyed Ali Jafar Zaidi0Indranath Chatterjee1Samir Brahim Belhaouari2Institute of Computer ScienceDepartment of Computer EngineeringDivision of Information and Computing TechnologyIn 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.http://dx.doi.org/10.1155/2022/1209172 |
| spellingShingle | Syed Ali Jafar Zaidi Indranath Chatterjee Samir Brahim Belhaouari COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers Applied Computational Intelligence and Soft Computing |
| title | COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers |
| title_full | COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers |
| title_fullStr | COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers |
| title_full_unstemmed | COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers |
| title_short | COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers |
| title_sort | covid 19 tweets classification during lockdown period using machine learning classifiers |
| url | http://dx.doi.org/10.1155/2022/1209172 |
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