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Data on sentiments and emotions of olympic-themed tweets
Two code files and one dataset related to Olympic Twitter activity are the foundation for this article. Through Twitter's Spritzer streaming API (Application Programming Interface), we collected over 430 million tweets from May 12th, 2016 to September 12th, 2016 windowing the Rio de Janeiro Oly...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468189/ https://www.ncbi.nlm.nih.gov/pubmed/31016215 http://dx.doi.org/10.1016/j.dib.2019.103869 |
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author | Vertalka, Joshua J. Kassens-Noor, Eva Wilson, Mark |
author_facet | Vertalka, Joshua J. Kassens-Noor, Eva Wilson, Mark |
author_sort | Vertalka, Joshua J. |
collection | PubMed |
description | Two code files and one dataset related to Olympic Twitter activity are the foundation for this article. Through Twitter's Spritzer streaming API (Application Programming Interface), we collected over 430 million tweets from May 12th, 2016 to September 12th, 2016 windowing the Rio de Janeiro Olympics and Paralympics. We cleaned and filtered these tweets to contain Olympic-related content. We then analyzed the raw data of 21,218,652 tweets including location data, language, and tweet content to distill the sentiment and emotions of Twitter users pertaining to the Olympic Games Kassens-Noor E. et al., 2019. We generalized the original data set to comply with the Twitter's Terms of Service and Developer agreement, 2018. We present the modified dataset and accompanying code files in this article to suggest using both for further analysis on sentiment and emotions related to the Rio de Janeiro Olympics and for comparative research on imagery and perceptions of other Olympic Games. |
format | Online Article Text |
id | pubmed-6468189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-64681892019-04-23 Data on sentiments and emotions of olympic-themed tweets Vertalka, Joshua J. Kassens-Noor, Eva Wilson, Mark Data Brief Social Science Two code files and one dataset related to Olympic Twitter activity are the foundation for this article. Through Twitter's Spritzer streaming API (Application Programming Interface), we collected over 430 million tweets from May 12th, 2016 to September 12th, 2016 windowing the Rio de Janeiro Olympics and Paralympics. We cleaned and filtered these tweets to contain Olympic-related content. We then analyzed the raw data of 21,218,652 tweets including location data, language, and tweet content to distill the sentiment and emotions of Twitter users pertaining to the Olympic Games Kassens-Noor E. et al., 2019. We generalized the original data set to comply with the Twitter's Terms of Service and Developer agreement, 2018. We present the modified dataset and accompanying code files in this article to suggest using both for further analysis on sentiment and emotions related to the Rio de Janeiro Olympics and for comparative research on imagery and perceptions of other Olympic Games. Elsevier 2019-03-20 /pmc/articles/PMC6468189/ /pubmed/31016215 http://dx.doi.org/10.1016/j.dib.2019.103869 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Social Science Vertalka, Joshua J. Kassens-Noor, Eva Wilson, Mark Data on sentiments and emotions of olympic-themed tweets |
title | Data on sentiments and emotions of olympic-themed tweets |
title_full | Data on sentiments and emotions of olympic-themed tweets |
title_fullStr | Data on sentiments and emotions of olympic-themed tweets |
title_full_unstemmed | Data on sentiments and emotions of olympic-themed tweets |
title_short | Data on sentiments and emotions of olympic-themed tweets |
title_sort | data on sentiments and emotions of olympic-themed tweets |
topic | Social Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468189/ https://www.ncbi.nlm.nih.gov/pubmed/31016215 http://dx.doi.org/10.1016/j.dib.2019.103869 |
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