Earticle

초록

영어

This study aims to collect and analyze unstructured customer review data to analyze user experiences on global fandom platforms like Weverse, Bubble, and Mubeat. The research aims to analyze user review data on fandom platforms using a text mining technique based on Latent Dirichlet Allocation (LDA). Considering user experience reviews, the study aims to derive insights for service improvement and platform competitiveness. The analysis revealed that “communication”, “convenience”, and “fandom identity” were the main themes identified in positive reviews. The negative reviews identified “login and account errors”, “translation and subtitle quality issues”, and “decreased usability after updates” as key issues. This study identified technical stability and translation quality improvements as urgent challenges for global fandom platforms. Therefore, platform operators must ensure stable service quality and provide customized features for global users. This can contribute to the continued growth of global fandoms and strengthen the competitiveness of the K-content industry.

목차

Abstract
1. 서론
2. 이론적 배경
2.1 글로벌 팬덤 플랫폼에 대한 연구
2.2 리뷰 데이터 기반의 텍스트 분석에 관한 연구
3. 연구방법
3.1 연구 프로세스
3.2 데이터 수집과 데이터 전처리
3.3 토픽 수 결정
4. 분석결과
5. 엔터테인먼트 산업 분석과 서비스 전략
6. 결론
References

키워드

저자

• 노미진 [ Mijin Noh | Assistant Professor, Keimyung University, Department of Business Big Data ] First Author
• 박순창 [ Soon Chang Park | Professor, Hyupsung University, Department of Business Administration ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005