Recent Research Trend Analysis for the Journal of Society of Korea Industrial and Systems Engineering Using Topic Modeling

서지정보

· 발행기관 : 한국산업경영시스템학회
· 수록지 정보 : 산업경영시스템학회지 / 46권 / 3호
· 저자명 : Dong Joon Park, Pyung Hoi Koo, Hyung Sool Oh, Min Yoon

목차

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

영어초록

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.

참고자료

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