LDA-based topic modeling for COVID-19-related sports research trends

Lee, Jea-Woog; Kim, Youngbin; Han, Doug Hyun

doi:10.3389/fpsyg.2022.1033872

Cited by 8 publications

(3 citation statements)

References 62 publications

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“…Second, topic analysis [ [28] , [29] , [30] , [31] , [32] ] was used to explore public and medical professionals’ concerns. The Bayesian LDA models were used to generate different topic clustering parameters by adjusting the number of hidden topics (represented by k values) [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias

Zhou,

Xu,

Yang

et al. 2024

International Journal of Nursing Sciences

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Section: Methodsmentioning

confidence: 99%

Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias

Zhou,

Xu,

Yang

et al. 2024

International Journal of Nursing Sciences

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“…This is because the proposed model can adequately analyze the correlation between words, the distribution of weights between words and their semantic similarities. In addition, Lee et al [23] tried to analyze trends in sports research topics during Covid-19 using the LDA method. A total of 1604 sports-related research articles were collected and pre-processed to obtain abstract information and keywords.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Topic Modelling to Analyze Biomedical Research Trends from the PubMed Database Using LDA Method

Pamungkas

2024

SISFOKOM

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Biomedical research has become an essential entity in human life. However, finding trends related to research topics in the health sector contained in the repository is a challenging matter. In this study, we implemented topic modelling to analyze biomedical research trends using the LDA method. Topic modelling was carried out using data from 7000 articles from PubMed, which were processed with text processing such as lowercase, punctuation removal, tokenization, stop-word removal, and lemmatization. For topic modelling, the LDA with corpus conditions varied to 75% and 100% for validation. Alpha and beta parameters are also set with variations between 0.01, 0.31, 0.61, 0.91, symmetry, and asymmetry when the number of the corpus is changed. When the number of the corpus is 75%, the optimal number of topics is 7, with a coherence value of 0.52. Whereas when the number of the corpus is 100%, the optimal number of topics is 10 with a coherence value of 0.51. In addition, based on the results of article topic modelling, several topics are trending, including disease diagnosis, patient care, and genetic or cell research. Based on the classification of biomedical topics into seven categories, the optimal accuracy, precision, and recall values using the Random Forest algorithm were obtained, namely 85.57%, 87.36%, and 87.58%. The results of this study suggest that topic modelling using the LDA can be used to identify trends in biomedical research with high accuracy. This information can help stakeholders make informed decisions about the direction of future research.

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“…While statistical significance is difficult to ascertain due to the erratic nature of the soccer coaching research field due to these internal and external factors, the highest publication and rapid increase in frequency is the most widespread research area. The post-COVID-19 changes can also be noted, as coaching and training-related research topics have been influenced by the overall research environment in sport during the COVID-19 pandemic compared to pre-COVID-19 [85]. In the post-pandemic era, it is worth noting whether the coaching and training environment has returned to pre-pandemic norms or whether new paradigms are being applied [86].…”

Section: Topic Area 2: Coaching Essentials In Footballmentioning

confidence: 99%

Data Analysis of Psychological Approaches to Soccer Research: Using LDA Topic Modeling

Lee,

Han

2023

Behavioral Sciences

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This study identifies the topical areas of research that have attempted a psychological approach to soccer research over the last 33 years (1990–2022) and explored the growth and stagnation of the topic as well as research contributions to soccer development. Data were obtained from 1863 papers from the Web of Science database. The data were collected through keyword text mining and data preprocessing to determine the keywords needed for analysis. Based on the keywords, latent Dirichlet allocation-based topic modeling analysis was performed to analyze the topic distribution of papers and explore research trends by topic area. The topic modeling process included four topic area and fifty topics. The “Coaching Essentials in Football” topic area had the highest frequency, but it was not statistically identified as a trend. However, coaching, including training, is expected to continue to be an important research topic, as it is a key requirement for success in the highly competitive elite football world. Interest in the research field of “Psychological Skills for Performance Development” has waned in recent years. This may be due to the predominance of other subject areas rather than a lack of interest. Various high-tech interventions and problem-solving attempts are being made in this field, providing opportunities for qualitative and quantitative expansion. “Motivation, cognition, and emotion” is a largely underrated subject area in soccer psychology. This could be because survey-based psychological evaluation attempts have decreased as the importance of rapid field application has been emphasized in recent soccer-related studies. However, measuring psychological factors contributes to the study of football psychology through a new methodology and theoretical background. Recognizing the important role of psychological factors in player performance and mental management, as well as presenting new research directions and approaches that can be directly applied to the field, will advance soccer psychology research.

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LDA-based topic modeling for COVID-19-related sports research trends

Cited by 8 publications

References 62 publications

Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias

Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias

Leveraging Topic Modelling to Analyze Biomedical Research Trends from the PubMed Database Using LDA Method

Data Analysis of Psychological Approaches to Soccer Research: Using LDA Topic Modeling

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