[논문]텍스트마이닝을 활용한 Covid-19 기간 동안의 항공산업 관련 키워드 트렌드 분석

최동현; 송보미; 박다현; 이성우

doi:10.9723/jksiis.2022.27.2.115

텍스트마이닝을 활용한 Covid-19 기간 동안의 항공산업 관련 키워드 트렌드 분석
Keyword trends analysis related to the aviation industry during the Covid-19 period using text mining 원문보기

한국산업정보학회논문지 = Journal of the Korea Industrial Information Systems Research, v.27 no.2, 2022년, pp.115 - 128

최동현 (한국항공대학교 항공교통물류학과) , 송보미 (한국항공대학교 항공교통물류학과) , 박다현 (한국항공대학교 항공교통물류학부) , 이성우 (한국항공대학교 항공교통물류학과)

초록
AI-Helper

본 연구는 Covid-19 팬데믹이 항공산업에 미친 영향과 동향을 살펴보고자 국내 뉴스 기사 데이터를 활용하여 키워드 트렌드 분석을 진행하였다. 데이터 수집을 위하여 Covid-19 발생 기준으로 전, 후 각 6개월의 기간을 나누어 '항공사' 키워드를 중심으로 관련 기사들을 추출하였다. 이후 기간별 동시 출현 빈도를 파악한 후 LDA 기법을 이용하여 토픽 모델링을 진행하였으며, Covid-19의 진행 동향과 토픽 패턴과의 관계 분석을 통해 상황에 따른 주요 토픽을 도출하였다. 이러한 결과를 활용하여 Covid-19와 같이 범세계적으로 영향을 주는 전염병이 발생할 경우 그 추이에 따라 항공산업에 미치는 영향을 예측할 수 있는 기초자료로 활용될 수 있을 것으로 기대된다.

Abstract ▼ AI-Helper

The purpose of this study is to conduct keyword trend analysis using articles data on the impact of Covid-19 in the aviation in dustry. In this study, related articles were extracted centering on the keyword "Airline" by dividing the period of 6months before and after Covid-19 occurrence. After that, Topic modeling(LDA) was performed. Through this, The main topic was extracted in the event of an epidemic such as Covid-19, It is expected to be used as primary data to predict the aviation industry's impact when occurrence like Covid-19.

주제어

표/그림 (10)

그림 Fig. 1 Stages of Text Mining Based Trend Analysis
그림 Fig. 2 Keyword Frequency (First stage)
그림 Fig. 3 Keyword Frequency (Second stage)
그림 Fig. 4 Keyword Frequency (Third stage)
그림 Fig. 5 Verification of topic consistency (First Step)
그림 Fig. 6 Verification of topic consistency (Second Step)
그림 Fig. 7 Verification of topic consistency (Third Step)
표 Table 1 Topics and key words in topics (First step)
표 Table 2 Topics and key words in topics (Second step)
표 Table 3 Topics and key words in topics (Third step)

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