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A Keyword Analysis Study on Postpartum Obesity Using Big Data

This study selected Google and Naver, the most recognizable Internet portals in Korea, as subjects for analysis. “Postpartum obesity” and “postpartum depression” were used as keywords for data collection. This study aimed to provide basic data for solving maternal problems using big data. Keywords r...

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Detalles Bibliográficos
Autores principales: Baik, Hyung-ui, Seo, Bo-Kyung, Kim, Gyu-Ri, Ku, Jung-Eun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8394976/
https://www.ncbi.nlm.nih.gov/pubmed/34444551
http://dx.doi.org/10.3390/ijerph18168807
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author Baik, Hyung-ui
Seo, Bo-Kyung
Kim, Gyu-Ri
Ku, Jung-Eun
author_facet Baik, Hyung-ui
Seo, Bo-Kyung
Kim, Gyu-Ri
Ku, Jung-Eun
author_sort Baik, Hyung-ui
collection PubMed
description This study selected Google and Naver, the most recognizable Internet portals in Korea, as subjects for analysis. “Postpartum obesity” and “postpartum depression” were used as keywords for data collection. This study aimed to provide basic data for solving maternal problems using big data. Keywords related to postpartum obesity were collected from the portal site Google from 1 January 2019 to 31 December 2019. The collected data were analyzed through simple frequency analysis, N-gram analysis, and keyword network. This study can be used as basic data for postpartum obesity-related programs or academic research. It is also expected to be used for research on the development of a mobile-based customized healthcare system focused on maternal health. Previous papers and data are still insufficient at solving the physical and mental problems related to postpartum obesity and depression. It is necessary to find ways to continuously integrate and collect data from mothers across the country.
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spelling pubmed-83949762021-08-28 A Keyword Analysis Study on Postpartum Obesity Using Big Data Baik, Hyung-ui Seo, Bo-Kyung Kim, Gyu-Ri Ku, Jung-Eun Int J Environ Res Public Health Article This study selected Google and Naver, the most recognizable Internet portals in Korea, as subjects for analysis. “Postpartum obesity” and “postpartum depression” were used as keywords for data collection. This study aimed to provide basic data for solving maternal problems using big data. Keywords related to postpartum obesity were collected from the portal site Google from 1 January 2019 to 31 December 2019. The collected data were analyzed through simple frequency analysis, N-gram analysis, and keyword network. This study can be used as basic data for postpartum obesity-related programs or academic research. It is also expected to be used for research on the development of a mobile-based customized healthcare system focused on maternal health. Previous papers and data are still insufficient at solving the physical and mental problems related to postpartum obesity and depression. It is necessary to find ways to continuously integrate and collect data from mothers across the country. MDPI 2021-08-20 /pmc/articles/PMC8394976/ /pubmed/34444551 http://dx.doi.org/10.3390/ijerph18168807 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Baik, Hyung-ui
Seo, Bo-Kyung
Kim, Gyu-Ri
Ku, Jung-Eun
A Keyword Analysis Study on Postpartum Obesity Using Big Data
title A Keyword Analysis Study on Postpartum Obesity Using Big Data
title_full A Keyword Analysis Study on Postpartum Obesity Using Big Data
title_fullStr A Keyword Analysis Study on Postpartum Obesity Using Big Data
title_full_unstemmed A Keyword Analysis Study on Postpartum Obesity Using Big Data
title_short A Keyword Analysis Study on Postpartum Obesity Using Big Data
title_sort keyword analysis study on postpartum obesity using big data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8394976/
https://www.ncbi.nlm.nih.gov/pubmed/34444551
http://dx.doi.org/10.3390/ijerph18168807
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