COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers
<p dir="ltr">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 peo...
محفوظ في:
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| مؤلفون آخرون: | , |
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2022
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إضافة وسم
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| _version_ | 1864513506580103168 |
|---|---|
| author | Syed Ali Jafar Zaidi (19505617) |
| author2 | Indranath Chatterjee (15374848) Samir Brahim Belhaouari (16855434) |
| author2_role | author author |
| author_facet | Syed Ali Jafar Zaidi (19505617) Indranath Chatterjee (15374848) Samir Brahim Belhaouari (16855434) |
| author_role | author |
| dc.creator.none.fl_str_mv | Syed Ali Jafar Zaidi (19505617) Indranath Chatterjee (15374848) Samir Brahim Belhaouari (16855434) |
| dc.date.none.fl_str_mv | 2022-07-07T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1155/2022/1209172 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/COVID-19_Tweets_Classification_during_Lockdown_Period_Using_Machine_Learning_Classifiers/26870437 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Pandemic Social Media Machine Learning (ML) Deep Learning (DL) Support Vector Machine (SVM) Tweets Dataset Kaggle |
| dc.title.none.fl_str_mv | COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Computational Intelligence and Soft Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1155/2022/1209172" target="_blank">https://dx.doi.org/10.1155/2022/1209172</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6637216e90843192573d730f8b33dc59 |
| identifier_str_mv | 10.1155/2022/1209172 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26870437 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | COVID-19 Tweets Classification during Lockdown Period Using Machine Learning ClassifiersSyed Ali Jafar Zaidi (19505617)Indranath Chatterjee (15374848)Samir Brahim Belhaouari (16855434)Information and computing sciencesArtificial intelligenceData management and data sciencePandemicSocial MediaMachine Learning (ML)Deep Learning (DL)Support Vector Machine (SVM)Tweets DatasetKaggle<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Computational Intelligence and Soft Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1155/2022/1209172" target="_blank">https://dx.doi.org/10.1155/2022/1209172</a></p>2022-07-07T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1155/2022/1209172https://figshare.com/articles/journal_contribution/COVID-19_Tweets_Classification_during_Lockdown_Period_Using_Machine_Learning_Classifiers/26870437CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268704372022-07-07T03:00:00Z |
| spellingShingle | COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers Syed Ali Jafar Zaidi (19505617) Information and computing sciences Artificial intelligence Data management and data science Pandemic Social Media Machine Learning (ML) Deep Learning (DL) Support Vector Machine (SVM) Tweets Dataset Kaggle |
| status_str | publishedVersion |
| 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 |
| topic | Information and computing sciences Artificial intelligence Data management and data science Pandemic Social Media Machine Learning (ML) Deep Learning (DL) Support Vector Machine (SVM) Tweets Dataset Kaggle |