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Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective

Since the advent of the worldwide COVID‐19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision‐makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using...

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Autores principales: Rahman, Md Mahbubar, Khan, Nafiz Imtiaz, Sarker, Iqbal H., Ahmed, Mohiuddin, Islam, Muhammad Nazrul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538004/
https://www.ncbi.nlm.nih.gov/pubmed/36247344
http://dx.doi.org/10.1002/eng2.12572
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author Rahman, Md Mahbubar
Khan, Nafiz Imtiaz
Sarker, Iqbal H.
Ahmed, Mohiuddin
Islam, Muhammad Nazrul
author_facet Rahman, Md Mahbubar
Khan, Nafiz Imtiaz
Sarker, Iqbal H.
Ahmed, Mohiuddin
Islam, Muhammad Nazrul
author_sort Rahman, Md Mahbubar
collection PubMed
description Since the advent of the worldwide COVID‐19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision‐makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state‐of‐the‐art technologies has been focused on during the COVID‐19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID‐19 pandemic from a cross‐country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1‐score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID‐19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like.
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spelling pubmed-95380042022-10-11 Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective Rahman, Md Mahbubar Khan, Nafiz Imtiaz Sarker, Iqbal H. Ahmed, Mohiuddin Islam, Muhammad Nazrul Eng Rep Research Articles Since the advent of the worldwide COVID‐19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision‐makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state‐of‐the‐art technologies has been focused on during the COVID‐19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID‐19 pandemic from a cross‐country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1‐score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID‐19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like. John Wiley and Sons Inc. 2022-09-18 /pmc/articles/PMC9538004/ /pubmed/36247344 http://dx.doi.org/10.1002/eng2.12572 Text en © 2022 The Authors. Engineering Reports published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Rahman, Md Mahbubar
Khan, Nafiz Imtiaz
Sarker, Iqbal H.
Ahmed, Mohiuddin
Islam, Muhammad Nazrul
Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective
title Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective
title_full Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective
title_fullStr Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective
title_full_unstemmed Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective
title_short Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective
title_sort leveraging machine learning to analyze sentiment from covid‐19 tweets: a global perspective
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538004/
https://www.ncbi.nlm.nih.gov/pubmed/36247344
http://dx.doi.org/10.1002/eng2.12572
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