The advent of big data has brought about the need for analytics. Natural language processing (NLP), a field of big data, has received a lot of attention. Topic modeling among NLP is widely applied to identify key topics in various academic journals. The Korean Society of Industrial and Systems Engineering (KSIE) has published academic journals since 1978. To enhance its status, it is imperative to recognize the diversity of research domains. We have already discovered eight major research topics for papers published by KSIE from 1978 to 1999. As a follow-up study, we aim to identify major topics of research papers published in KSIE from 2000 to 2022. We performed topic modeling on 1,742 research papers during this period by using LDA and BERTopic which has recently attracted attention. BERTopic outperformed LDA by providing a set of coherent topic keywords that can effectively distinguish 36 topics found out this study. In terms of visualization techniques, pyLDAvis presented better two-dimensional scatter plots for the intertopic distance map than BERTopic. However, BERTopic provided much more diverse visualization methods to explore the relevance of 36 topics. BERTopic was also able to classify hot and cold topics by presenting ‘topic over time’ graphs that can identify topic trends over time.

1. 서 론
2. 이론적 배경
    2.1 선행연구
    2.2 LDA와 BERTOPIC
3. 연구방법
    3.1 연구개요
    3.2 전처리(preprocess)
    3.3 시각화
4. 연구결과
    4.1 연구논문 현황
    4.2 LDA와 BERTOPIC의 튜닝과 주제 개수의결정
    4.3 LDA 분석 결과
    4.4 BERTopic 분석 결과
5. 결 론
    5.1 분석 결과의 요약
    5.2 연구의 의의 및 제한점
Acknowledgement

• Dong Joon Park(Major of Statistics and Data Science, Pukyong National University) | 박동준 (부경대학교 통계·데이터사이언스 전공)
• Pyung Hoi Koo(Division of Systems Management and Safety Engineering, Pukyong National University) | 구평회 (부경대학교 시스템경영·안전공학부)
• Hyung Sool Oh(Department of AI & Software, Kangwon National University) | 오형술 (강원대학교 AI소프트웨어학과)
• Min Yoon(Department of Applied Mathematics, Pukyong National University) | 윤민 (부경대학교 응용수학과) Corresponding author