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Dataset of two decades of Tiger Woods press conferences and tournament performance

This data article describes a dataset that allows exploring the determinants of superstars’ sentiment in tournaments. It consists of 1,284 press conferences of Tiger Woods in the PGA Tour between 1996 and 2020. We used natural language processing, a form of artificial intelligence, to extract and en...

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Detalles Bibliográficos
Autores principales: Pastoriza, David, Warin, Thierry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889344/
https://www.ncbi.nlm.nih.gov/pubmed/35252489
http://dx.doi.org/10.1016/j.dib.2022.107955
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author Pastoriza, David
Warin, Thierry
author_facet Pastoriza, David
Warin, Thierry
author_sort Pastoriza, David
collection PubMed
description This data article describes a dataset that allows exploring the determinants of superstars’ sentiment in tournaments. It consists of 1,284 press conferences of Tiger Woods in the PGA Tour between 1996 and 2020. We used natural language processing, a form of artificial intelligence, to extract and encode in a quantitative form the sentiment in Tiger Woods press conferences both before the tournament and after the rounds played. Additionally, the dataset provides a series of variables that describe Tiger Woods’ scoring and performance momentum in each round and variables that describe health-related and off-the-course issues that could affect his performance on the course. This data can be useful to understand the sentiment that superstars go through before important tournaments, their sentiment following a major victory or defeat, how that sentiment evolves throughout their athletic career, and how sentiment is associated with performance momentum.
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spelling pubmed-88893442022-03-03 Dataset of two decades of Tiger Woods press conferences and tournament performance Pastoriza, David Warin, Thierry Data Brief Data Article This data article describes a dataset that allows exploring the determinants of superstars’ sentiment in tournaments. It consists of 1,284 press conferences of Tiger Woods in the PGA Tour between 1996 and 2020. We used natural language processing, a form of artificial intelligence, to extract and encode in a quantitative form the sentiment in Tiger Woods press conferences both before the tournament and after the rounds played. Additionally, the dataset provides a series of variables that describe Tiger Woods’ scoring and performance momentum in each round and variables that describe health-related and off-the-course issues that could affect his performance on the course. This data can be useful to understand the sentiment that superstars go through before important tournaments, their sentiment following a major victory or defeat, how that sentiment evolves throughout their athletic career, and how sentiment is associated with performance momentum. Elsevier 2022-02-15 /pmc/articles/PMC8889344/ /pubmed/35252489 http://dx.doi.org/10.1016/j.dib.2022.107955 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Data Article
Pastoriza, David
Warin, Thierry
Dataset of two decades of Tiger Woods press conferences and tournament performance
title Dataset of two decades of Tiger Woods press conferences and tournament performance
title_full Dataset of two decades of Tiger Woods press conferences and tournament performance
title_fullStr Dataset of two decades of Tiger Woods press conferences and tournament performance
title_full_unstemmed Dataset of two decades of Tiger Woods press conferences and tournament performance
title_short Dataset of two decades of Tiger Woods press conferences and tournament performance
title_sort dataset of two decades of tiger woods press conferences and tournament performance
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889344/
https://www.ncbi.nlm.nih.gov/pubmed/35252489
http://dx.doi.org/10.1016/j.dib.2022.107955
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