Topical Landscape Analysis of Korean Fashion Cluster Research Using Text Mining Techniques

This study conducted a text mining analysis of academic papers on Korean fashion clusters to gain a multilayered understanding of the topical landscape. To achieve this, a step-by-step approach was implemented, including Count-Vectorizer-based keyword analysis, TF-IDF weighting, word cloud visualization, and network centrality analysis. The results indicated the following. First, contrary to expectations, research on fashion clusters has been relatively limited annually. However, it showed a trend toward a multidisciplinary perspective, encompassing not only clothing and textiles but also urban planning, architecture, economic geography, policy studies, sociology, and cultural studies. Second, Korean fashion cluster research focused on the core concepts of &quotindustry,&quot &quotfashion,&quot &quotcluster,&quot and &quotregion,&quot demonstrating high TF-IDF values and centrality. Third, the same keywords also ranked highly in degree and closeness centrality analyses. This suggests that fashion cluster research topics are relatively balanced, focusing on industrial structures, regional production ecosystems, and cluster formation factors. In the betweenness centrality analysis, words such as &quotanalysis,&quot &quotproduction,&quot and &quotstrategy&quot played a mediating role, bridging semantic connections across research areas. Eigenvector centrality analysis identified &quotfashion,&quot &quotcluster,&quot and &quotregion&quot as the most influential nodes. Finally, an examination of the keyword network based on degree centrality revealed a hierarchical structure, with &quotindustry,&quot &quotregion,&quot and &quotcluster&quot forming the central axis and multilayered connections among manufacturing infrastructure, policies, and the economic environment. &quotFashion,&quot &quotcluster,&quot and &quotregion&quot formed the largest nodes within the network.<br/>Dongdaemun, with its connections to numerous concepts, plays a crucial role in discussions of domestic fashion clusters. These results demonstrate that fashion clusters are evolving beyond production hubs into industrial ecosystems that mediate complex structures such as regional economies, industrial policies, and global supply chains.

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Publication Details

Journal
The Korean Society of Costume
Published
2026-02-28
DOI
https://doi.org/10.7233/jksc.2026.76.1.037
Primary Topic
Diverse Topics in Contemporary Research
Type
article
Field-Weighted Citation Impact
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article

Topical Landscape Analysis of Korean Fashion Cluster Research Using Text Mining Techniques

Hyeri Joo, Jinah Son
The Korean Society of Costume
Diverse Topics in Contemporary Research
article

Topical Landscape Analysis of Korean Fashion Cluster Research Using Text Mining Techniques

Hyeri Joo, Jinah Son
article en

Abstract

This study conducted a text mining analysis of academic papers on Korean fashion clusters to gain a multilayered understanding of the topical landscape. To achieve this, a step-by-step approach was implemented, including Count-Vectorizer-based keyword analysis, TF-IDF weighting, word cloud visualization, and network centrality analysis. The results indicated the following. First, contrary to expectations, research on fashion clusters has been relatively limited annually. However, it showed a trend toward a multidisciplinary perspective, encompassing not only clothing and textiles but also urban planning, architecture, economic geography, policy studies, sociology, and cultural studies. Second, Korean fashion cluster research focused on the core concepts of &quotindustry,&quot &quotfashion,&quot &quotcluster,&quot and &quotregion,&quot demonstrating high TF-IDF values and centrality. Third, the same keywords also ranked highly in degree and closeness centrality analyses. This suggests that fashion cluster research topics are relatively balanced, focusing on industrial structures, regional production ecosystems, and cluster formation factors. In the betweenness centrality analysis, words such as &quotanalysis,&quot &quotproduction,&quot and &quotstrategy&quot played a mediating role, bridging semantic connections across research areas. Eigenvector centrality analysis identified &quotfashion,&quot &quotcluster,&quot and &quotregion&quot as the most influential nodes. Finally, an examination of the keyword network based on degree centrality revealed a hierarchical structure, with &quotindustry,&quot &quotregion,&quot and &quotcluster&quot forming the central axis and multilayered connections among manufacturing infrastructure, policies, and the economic environment. &quotFashion,&quot &quotcluster,&quot and &quotregion&quot formed the largest nodes within the network.<br/>Dongdaemun, with its connections to numerous concepts, plays a crucial role in discussions of domestic fashion clusters. These results demonstrate that fashion clusters are evolving beyond production hubs into industrial ecosystems that mediate complex structures such as regional economies, industrial policies, and global supply chains.

The Korean Society of CostumeVol. 76(1)
Industry, innovation and infrastructure
Openalex Percentile: Top 27%
Diverse Topics in Contemporary Research
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