Cargando…

COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. T...

Descripción completa

Detalles Bibliográficos
Autores principales: Jalil, Zunera, Abbasi, Ahmed, Javed, Abdul Rehman, Badruddin Khan, Muhammad, Abul Hasanat, Mozaherul Hoque, Malik, Khalid Mahmood, Saudagar, Abdul Khader Jilani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795663/
https://www.ncbi.nlm.nih.gov/pubmed/35096755
http://dx.doi.org/10.3389/fpubh.2021.812735
_version_ 1784641120941113344
author Jalil, Zunera
Abbasi, Ahmed
Javed, Abdul Rehman
Badruddin Khan, Muhammad
Abul Hasanat, Mozaherul Hoque
Malik, Khalid Mahmood
Saudagar, Abdul Khader Jilani
author_facet Jalil, Zunera
Abbasi, Ahmed
Javed, Abdul Rehman
Badruddin Khan, Muhammad
Abul Hasanat, Mozaherul Hoque
Malik, Khalid Mahmood
Saudagar, Abdul Khader Jilani
author_sort Jalil, Zunera
collection PubMed
description The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.
format Online
Article
Text
id pubmed-8795663
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-87956632022-01-29 COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques Jalil, Zunera Abbasi, Ahmed Javed, Abdul Rehman Badruddin Khan, Muhammad Abul Hasanat, Mozaherul Hoque Malik, Khalid Mahmood Saudagar, Abdul Khader Jilani Front Public Health Public Health The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms. Frontiers Media S.A. 2022-01-14 /pmc/articles/PMC8795663/ /pubmed/35096755 http://dx.doi.org/10.3389/fpubh.2021.812735 Text en Copyright © 2022 Jalil, Abbasi, Javed, Badruddin Khan, Abul Hasanat, Malik and Saudagar. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Jalil, Zunera
Abbasi, Ahmed
Javed, Abdul Rehman
Badruddin Khan, Muhammad
Abul Hasanat, Mozaherul Hoque
Malik, Khalid Mahmood
Saudagar, Abdul Khader Jilani
COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques
title COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques
title_full COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques
title_fullStr COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques
title_full_unstemmed COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques
title_short COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques
title_sort covid-19 related sentiment analysis using state-of-the-art machine learning and deep learning techniques
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795663/
https://www.ncbi.nlm.nih.gov/pubmed/35096755
http://dx.doi.org/10.3389/fpubh.2021.812735
work_keys_str_mv AT jalilzunera covid19relatedsentimentanalysisusingstateoftheartmachinelearninganddeeplearningtechniques
AT abbasiahmed covid19relatedsentimentanalysisusingstateoftheartmachinelearninganddeeplearningtechniques
AT javedabdulrehman covid19relatedsentimentanalysisusingstateoftheartmachinelearninganddeeplearningtechniques
AT badruddinkhanmuhammad covid19relatedsentimentanalysisusingstateoftheartmachinelearninganddeeplearningtechniques
AT abulhasanatmozaherulhoque covid19relatedsentimentanalysisusingstateoftheartmachinelearninganddeeplearningtechniques
AT malikkhalidmahmood covid19relatedsentimentanalysisusingstateoftheartmachinelearninganddeeplearningtechniques
AT saudagarabdulkhaderjilani covid19relatedsentimentanalysisusingstateoftheartmachinelearninganddeeplearningtechniques