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Sentimental and spatial analysis of COVID-19 vaccines tweets

The world has to face health concerns due to huge spread of COVID. For this reason, the development of vaccine is the need of hour. The higher vaccine distribution, the higher the immunity against coronavirus. Therefore, there is a need to analyse the people’s sentiment for the vaccine campaign. Tod...

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Autores principales: Umair, Areeba, Masciari, Elio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012072/
https://www.ncbi.nlm.nih.gov/pubmed/35462784
http://dx.doi.org/10.1007/s10844-022-00699-4
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author Umair, Areeba
Masciari, Elio
author_facet Umair, Areeba
Masciari, Elio
author_sort Umair, Areeba
collection PubMed
description The world has to face health concerns due to huge spread of COVID. For this reason, the development of vaccine is the need of hour. The higher vaccine distribution, the higher the immunity against coronavirus. Therefore, there is a need to analyse the people’s sentiment for the vaccine campaign. Today, social media is the rich source of data where people share their opinions and experiences by their posts, comments or tweets. In this study, we have used the twitter data of vaccines of COVID and analysed them using methods of artificial intelligence and geo-spatial methods. We found the polarity of the tweets using the TextBlob() function and categorized them. Then, we designed the word clouds and classified the sentiments using the BERT model. We then performed the geo-coding and visualized the feature points over the world map. We found the correlation between the feature points geographically and then applied hotspot analysis and kernel density estimation to highlight the regions of positive, negative or neutral sentiments. We used precision, recall and F score to evaluate our model and compare our results with the state-of-the-art methods. The results showed that our model achieved 55% & 54% precision, 69% & 85% recall and 58% & 64% F score for positive class and negative class respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people’s attitudes towards the vaccines.
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spelling pubmed-90120722022-04-18 Sentimental and spatial analysis of COVID-19 vaccines tweets Umair, Areeba Masciari, Elio J Intell Inf Syst Article The world has to face health concerns due to huge spread of COVID. For this reason, the development of vaccine is the need of hour. The higher vaccine distribution, the higher the immunity against coronavirus. Therefore, there is a need to analyse the people’s sentiment for the vaccine campaign. Today, social media is the rich source of data where people share their opinions and experiences by their posts, comments or tweets. In this study, we have used the twitter data of vaccines of COVID and analysed them using methods of artificial intelligence and geo-spatial methods. We found the polarity of the tweets using the TextBlob() function and categorized them. Then, we designed the word clouds and classified the sentiments using the BERT model. We then performed the geo-coding and visualized the feature points over the world map. We found the correlation between the feature points geographically and then applied hotspot analysis and kernel density estimation to highlight the regions of positive, negative or neutral sentiments. We used precision, recall and F score to evaluate our model and compare our results with the state-of-the-art methods. The results showed that our model achieved 55% & 54% precision, 69% & 85% recall and 58% & 64% F score for positive class and negative class respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people’s attitudes towards the vaccines. Springer US 2022-04-15 2023 /pmc/articles/PMC9012072/ /pubmed/35462784 http://dx.doi.org/10.1007/s10844-022-00699-4 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Umair, Areeba
Masciari, Elio
Sentimental and spatial analysis of COVID-19 vaccines tweets
title Sentimental and spatial analysis of COVID-19 vaccines tweets
title_full Sentimental and spatial analysis of COVID-19 vaccines tweets
title_fullStr Sentimental and spatial analysis of COVID-19 vaccines tweets
title_full_unstemmed Sentimental and spatial analysis of COVID-19 vaccines tweets
title_short Sentimental and spatial analysis of COVID-19 vaccines tweets
title_sort sentimental and spatial analysis of covid-19 vaccines tweets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012072/
https://www.ncbi.nlm.nih.gov/pubmed/35462784
http://dx.doi.org/10.1007/s10844-022-00699-4
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