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COVID-19 vaccine sentiment analysis using public opinions on Twitter
Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We off...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046075/ https://www.ncbi.nlm.nih.gov/pubmed/35502322 http://dx.doi.org/10.1016/j.matpr.2022.04.809 |
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author | Chinnasamy, P. Suresh, V. Ramprathap, K. Jebamani, B. Jency A. Srinivas Rao, K. Shiva Kranthi, M. |
author_facet | Chinnasamy, P. Suresh, V. Ramprathap, K. Jebamani, B. Jency A. Srinivas Rao, K. Shiva Kranthi, M. |
author_sort | Chinnasamy, P. |
collection | PubMed |
description | Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs. |
format | Online Article Text |
id | pubmed-9046075 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90460752022-04-28 COVID-19 vaccine sentiment analysis using public opinions on Twitter Chinnasamy, P. Suresh, V. Ramprathap, K. Jebamani, B. Jency A. Srinivas Rao, K. Shiva Kranthi, M. Mater Today Proc Article Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs. Elsevier Ltd. 2022 2022-04-28 /pmc/articles/PMC9046075/ /pubmed/35502322 http://dx.doi.org/10.1016/j.matpr.2022.04.809 Text en Copyright © 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advanced Materials for Innovation and Sustainability. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Chinnasamy, P. Suresh, V. Ramprathap, K. Jebamani, B. Jency A. Srinivas Rao, K. Shiva Kranthi, M. COVID-19 vaccine sentiment analysis using public opinions on Twitter |
title | COVID-19 vaccine sentiment analysis using public opinions on Twitter |
title_full | COVID-19 vaccine sentiment analysis using public opinions on Twitter |
title_fullStr | COVID-19 vaccine sentiment analysis using public opinions on Twitter |
title_full_unstemmed | COVID-19 vaccine sentiment analysis using public opinions on Twitter |
title_short | COVID-19 vaccine sentiment analysis using public opinions on Twitter |
title_sort | covid-19 vaccine sentiment analysis using public opinions on twitter |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046075/ https://www.ncbi.nlm.nih.gov/pubmed/35502322 http://dx.doi.org/10.1016/j.matpr.2022.04.809 |
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