토픽모델링을 활용한 한국산업경영시스템학회지의 최근 연구주제 분석

• 영문명: Recent Research Trend Analysis for the Journal of Society of Korea Industrial and Systems Engineering Using Topic Modeling
• 발행기관: 한국산업경영시스템학회
• 저자명: 박동준 구평회 오형술 윤민
• 간행물 정보: 『산업경영시스템학회지』제46권 제3호, 170~185쪽, 전체 16쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.09.30

영문 초록

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.

목차

1. 서 론

2. 이론적 배경

3. 연구방법

4. 연구결과

5. 결 론

References

키워드

Topic Modeling BERTopic Latent Dirichlet Allocation Industrial Engineering