A text mining approach to the analysis of BTS fever

Soobin Choi, Gayeon Park, Hee Woong Kim

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

K-POP is steadily growing with global competitiveness. The rise of K-POP's popularity has continued to create Korean idol groups. However, many idol groups were dismantled and there is lack of measures for overseas advance and success. Therefore, this study aims to analyze the success factors of BTS by focusing on the text mining techniques. After collecting Twitter's online postings using crawling technique, we will analyze in three text mining techniques: topic modeling, keyword extraction, and term frequency analysis. By analyzing data with three text mining methods, we will derive how BTS could success globally and form a huge fandom. And with the derived key factors, we will suggest a success strategy based on the analysis results. In contrast to previous studies that were centered on case studies or interview, this study has implications in that the actual data was collected and analyzed through three text mining techniques.

Original languageEnglish
Publication statusPublished - 2019
Event23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019 - Xi'an, China
Duration: 2019 Jul 82019 Jul 12

Conference

Conference23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019
Country/TerritoryChina
CityXi'an
Period19/7/819/7/12

Bibliographical note

Publisher Copyright:
© Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019.

All Science Journal Classification (ASJC) codes

  • Information Systems

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