Physical Activity and Exercise: Text Mining Analysis

Abstract

It is currently difficult to have a global state of the art vision of certain scientific topics. In the field of physical activity (PA) and exercise, this is due to information overload. The present study aims to provide a solution by analysing a large mass of scientific articles using text mining (TM). The purpose was to analyse what is being investigated in the PA health field on young people from primary, secondary and higher education. Titles and abstracts published in the Web of Science (WOS) database were analysed using TM on 24 November 2020, and after removing duplicates, 85,368 remained. The results show 9960 (unique) words and the most frequently used bi-grams and tri-grams. A co-occurrence network was also generated. ‘Health’ was the first term of importance and the most repeated bi-grams and tri-grams were ‘body_mass’ and ‘body_mass_index’. The analyses of the 20 topics identified focused on health-related terms, the social sphere, sports performance and research processes. It also found that the terms health and exercise have become more important in recent years.

Exploring Korean adolescent stress on social media: a semantic network analysis

“…It used text-mining techniques and SNA for related big data collected from the Internet on adolescents’ dietary behaviours ( Song et al, 2022 ). A study on physical activity and exercise in school-age youth was conducted to provide a solution by analysing a large number of scientific articles through text mining ( Pans et al, 2021 ). In the belief that social media plays an important role in adolescents’ life, a study describing the big data approach to social media has also been presented by analysing an ad hoc dataset from the eating disorder forum of a social media website ( Moessner et al, 2018 ).…”

Network analysis based on big data in social media of Korean adolescents’ diet behaviors

“…Previous studies that attempted big data-based network analysis on adolescents had considered their peer relationships, smoking and drinking experiences [ 12 ], and peer networks according to their physical factors [ 9 ], and used semantic network analysis for assessing the knowledge structure of students with severe and multiple disabilities [ 13 ]. Another study on physical activity and exercise in school-aged youth aimed to provide a solution by analyzing a large number of scientific articles using text mining [ 14 ]. A study has also been conducted to analyze Korean adolescents’ perceptions of sports and physical activities through big data analysis over the last 10 years, and provide research data and statistical direction with regard to their participation in such activities [ 15 ].…”