Identifying Group Identities and Co-authorship Networks Analysis in Academic Journals Based on Natural Language Processing

Purpose: The aim of this study was to determine whether the “Asian Journal of Beauty and Cosmetology” and domestic journals that use “cosmetics” as a keyword in the abstract of their paper, are positioned with differentiated identities and to identify the similarities and differences between them. All articles were procured from the “Research Information Sharing Service.”Methods: Python version 3.10.9 was used in this study to identify 433 research papers from the “Asian Journal of Beauty and Cosmetology” and 5,232 research papers with the keyword “cosmetic” from the “Research Information Sharing Service.” After preprocessing the articles, a co-author network analysis was performed, followed by cluster analysis and topic modeling analysis. Four types of similarity analyses were performed based on the results obtained.Results: The two journal groups were found to commonly cover a variety of topics such as skin, consumer behavior, and brand strategy, that are central to the cosmetics industry. However, the research topics had different central focuses, indicating distinct identities.Conclusion: The centrality analysis results from the co-authorship network analysis revealed the roles and influence of each co-author in maximizing the synergy of research. The identity of the Asian Journal of Beauty and Cosmetology was found be unique and differentiated, compared with the comparison group.