자연어 처리 기술을 활용한 인구 고령화 관련 토픽 분석

• 영문명: Analysis of Topics Related to Population Aging Using Natural Language Processing Techniques
• 발행기관: 한국IT서비스학회
• 저자명: 박현정(Hyunjung Park) 이태민(Taemin Lee) 임희석(Heuiseok Lim)
• 간행물 정보: 『한국IT서비스학회지』제23권 제1호, 55~79쪽, 전체 25쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2024.02.28

영문 초록

Korea, which is expected to enter a super-aged society in 2025, is facing the most worrisome crisis worldwide. Efforts are urgently required to examine problems and countermeasures from various angles and to improve the shortcomings. In this regard, from a new viewpoint, we intend to derive useful implications by applying the recent natural language processing techniques to online articles. More specifically, we derive three research questions: First, what topics are being reported in the online media and what is the public's response to them? Second, what is the relationship between these aging-related topics and individual happiness factors? Third, what are the strategic directions and implications for benchmarking discussed to solve the problem of population aging?To find answers to these, we collect Naver portal articles related to population aging and their classification categories, comments, and number of comments, including other numerical data. From the data, we firstly derive 33 topics with a semi-supervised BERTopic by reflecting article classification information that was not used in previous studies, conducting sentiment analysis of comments on them with a current open-source large language model. We also examine the relationship between the derived topics and personal happiness factors extended to Alderfer's ERG dimension, carrying out additional 3~4-gram keyword frequency analysis, trend analysis, text network analysis based on 3~4-gram keywords, etc. Through this multifaceted approach, we present diverse fresh insights from practical and theoretical perspectives.

목차

1. 서론

2. 이론적 배경

3. 연구 방법

4. 연구 결과

5. 결론

참고문헌

키워드

Population Aging NLP Topic Modeling Semi-supervised BERTopic ERG Theory Sentiment Analysis Text Network Analysis Trend Analysis