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Severity Analysis of User Complaints in Negative Reviews of Fintech Payment Applications Using Topic Modeling and Large Language Models Min-Sol Kim․Ki-Kwang Lee† KCI 등재

토픽모델링과 대규모 언어모델을 활용한 핀테크 결제 앱 부정 리뷰의 사용자 불만 요소 심각도 분석

Min-Sol Kim, Ki-Kwang Lee
  • 언어KOR
  • URLhttps://db.koreascholar.com/Article/Detail/453350
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한국산업경영시스템학회지 (Journal of Society of Korea Industrial and Systems Engineering)
한국산업경영시스템학회 (Society of Korea Industrial and Systems Engineering)
초록

This study asks which complaints are most severe in negative reviews of three leading Korean fintech payment applications (Toss, Kakao Pay, and Naver Pay). Latent Dirichlet Allocation on 14,000 Google Play reviews yielded seven topics, to which a residual category was added, and a large language model assigned one of the eight resulting categories to every review. Six of these categories, referred to as complaint types, were divided into core and supplementary services following Wirtz and Lovelock's service structure, and severity was assessed by star rating and by stated intention to discontinue use. Supplementary types ranked highest on both measures, although the type with the highest share of one-star reviews differed by application. All 1,714 one-star reviews of those types were then analyzed qualitatively, and obstruction of the core service could be determined for 1,641. Discontinuation intention was stated more often where a supplementary element obstructed use of the core service. Within such obstruction it was concentrated in the design-induced form, in which the element worked as intended but its design or policy displaced the core service (22.2% vs. 13.2% for complaints involving no obstruction; common odds ratio 2.28, 95% CI 1.51–3.44, stratified by application). Frequency and severity therefore do not coincide: the most severe complaints arise where a supplementary element's design displaces the core service.

키워드
FintechApp ReviewTopic ModelingLarge Language ModelComplaint Severity
목차
1. 서 론
2. 이론적 배경 및 선행연구
    2.1 핀테크 앱과 사용자 리뷰
    2.2 리뷰 분석 방법과 분석 틀
3. 연구 방법
    3.1 데이터 수집
    3.2 리뷰 분류
    3.3 심각도 측정
    3.4 정성 분석
4. 연구 결과
    4.1 리뷰 범주의 분포
    4.2 유형별 심각도와 앱별 차이
    4.3 세부 내용과 핵심 서비스 저해
    4.4 저해 양상별 분석
5. 결 론
Acknowledgement
References
부 록
    부록 A. LDA 학습 설정과 토픽 수 결정(3.2절)
    부록 B. 토픽별 상위 단어와 비중(3.2절)
    부록 C. 언어모델 설정과 프롬프트(3.2절, 3.3절)
    부록 D. 세부 내용 도출의 수렴적 확인(3.4절)
    부록 E. 민감도 점검과 보조 분석(3.1절, 4.2절)
저자
  • Min-Sol Kim(School of Business Administration, Dankook University) | 김민솔 (단국대학교 경영학부)
  • Ki-Kwang Lee(School of Business Administration, Dankook University) | 이기광 (단국대학교 경영학부) Corresponding author