[논문]텍스트 분석을 이용한 코로나19 관련 국내 논문의 주제 및 감성에 관한 융합 연구

허성민; 양지연

doi:10.15207/jkcs.2021.12.4.031

텍스트 분석을 이용한 코로나19 관련 국내 논문의 주제 및 감성에 관한 융합 연구
A Convergence Study on the Topic and Sentiment of COVID19 Research in Korea Using Text Analysis 원문보기

한국융합학회논문지 = Journal of the Korea Convergence Society, v.12 no.4, 2021년, pp.31 - 42

허성민 (금오공과대학교 응용수학과) , 양지연 (금오공과대학교 응용수학과)

초록
AI-Helper

본 연구에서는 코로나19 관련 연구논문의 연구주제를 탐색하고 동향을 검토하고 있다. 또한 감성분석을 통해 부정적인 어조가 강한 경고가 되는 주제들을 알아본다. 잠재 디리슐레 할당(LDA)를 이용하여 총 8개의 토픽을 발견하였고, 이를 구조적 토픽 모델링(STM)과 비교하여 비교적 안정적인 결과임을 확인하였다. 또한 k-means 군집 알고리즘을 통해 각 토픽별로 세부 연구주제를 발견하였고 주성분 분석을 이용하여 이를 시각적으로 표현하였다. 감성분석을 통해 각 토픽별 긍정적, 부정적인 단어들을 살펴보고 감성점수를 계산하여 연구논문의 주된 어조를 파악하였는데, 특히 생물 의학 관련, 국제적 역학관계, 심리적 영향과 관련된 연구에서 부정적인 어조가 강한 것으로 나타나 해당 부문에 대해서 주의와 관심이 요구된다. 향후 연구자들이 연구의 방향성을 탐색하고 정책결정자들이 연구지원 사업을 결정하는데 기초자료로 활용될 수 있을 것이다.

Abstract ▼ AI-Helper

The purpose of this study was to explore research topics and examine the trend in COVID19 related research papers. We identified eight topics using latent Dirichlet allocation and found acceptable validity in comparison with the structural topic model. The subtopics have been extracted using k-means clustering and plotted in PCA space. Additionally, we discovered the topics bearing negative tones and warning signs by sentiment analysis. The results flagged up the issues of the topics, Biomedical Related, International Dynamics and Psychological Impact. The findings could serve as a guideline for researchers who explore new research directions and policymakers who need to make decisions about which research projects to support.

주제어

표/그림 (5)

그림 Fig. 1. Selection of the number of topics.
표 Table 1. The trend of topic appearance in percentages. Q1, Q2, Q3 and Q4 are the 1st, 2nd, 3rd and 4th quarter of 2020, respectively. T1: Economic Impact, T2: Biomedical Related, T3: Social Care & Welfare, T4: International Dynamics, T5: Religion Related, T6: Technology & Security, T7: Psychological Impact, T8: Online Education.
표 Table 2. The number of research papers induced by LDA and STM. T1: Economic Impact, T2: Biomedical Related, T3: Social Care & Welfare, T4: International Dynamics, T5: Religion Related, T6: Technology & Security, T7: Psychological Impact, T8: Online Education.
그림 Fig. 2. Identification of subtopics.
그림 Fig. 3. Sentiment analysis using Bing lexicon. A net score is computed as the difference between positive and negative word counts, with positive(negative) values implying positive(negative) stances and values close to zero implying neutral stances. The score averages of the four quarters of 2020 are connected with lines.

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