Journal of Korea Trade

Korea Trade Research Association (한국무역학회)
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Association Modeling on Keyword and Abstract Data in Korean Port Research

  • Yoon, Hee-Young (Department of Tax Accounting, Soongeui Women's College) ;
  • Kwak, Il-Youp (Department of Applied Statistics, Chung-Ang University)
  • Received : 2020.02.27
  • Accepted : 2020.08.07
  • Published : 2020.08.31
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Abstract

Purpose - This study investigates research trends by searching for English keywords and abstracts in 1,511 Korean journal articles in the Korea Citation Index from the 2002-2019 period using the term "Port." The study aims to lay the foundation for a more balanced development of port research. Design/methodology - Using abstract and keyword data, we perform frequency analysis and word embedding (Word2vec). A t-SNE plot shows the main keywords extracted using the TextRank algorithm. To analyze which words were used in what context in our two nine-year subperiods (2002-2010 and 2010-2019), we use Scattertext and scaled F-scores. Findings - First, during the 18-year study period, port research has developed through the convergence of diverse academic fields, covering 102 subject areas and 219 journals. Second, our frequency analysis of 4,431 keywords in 1,511 papers shows that the words "Port" (60 times), "Port Competitiveness" (33 times), and "Port Authority" (29 times), among others, are attractive to most researchers. Third, a word embedding analysis identifies the words highly correlated with the top eight keywords and visually shows four different subject clusters in a t-SNE plot. Fourth, we use Scattertext to compare words used in the two research sub-periods. Originality/value - This study is the first to apply abstract and keyword analysis and various text mining techniques to Korean journal articles in port research and thus has important implications. Further in-depth studies should collect a greater variety of textual data and analyze and compare port studies from different countries.

Keywords

Acknowledgement

This work was supported by the Chung-Ang University Research Grants in 2019.

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