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"Data mining"

Original Article
[Korean]
ABSTRACT Objectives:

Public health risks and anxiety have been increasing since the outbreak of Coronavirus disease 19 (COVID-19). The public expresses questions related to the COVID-19 issue through the web base. The aim of this study was to analyze public perception and sentiments of COVID-19 Pandemic in South Korea.

Methods:

We collected the text data (questions: 252,181) related to COVID-19 from Naver Knowledge-iN during January 1, 2020 to December 31, 2020. The search keywords included related to COVID-19 using Korean words for “SARS-Cov-2”, “COVID19”, “COVID-19”, “Wuhan pneumonia”, “Coronavirus”, “Corona”. A topic modeling analysis was used to investigate and search trends of public perception. The sentiment analysis was conducted to analyze of public emotions in the questions related to COVID-19. We performed the Pearson’s correlation analysis between daily number of COVID-19 cases and daily proportion of negative sentiment in documents related to COVID-19 by COVID-19 outbreak period.

Results:

A total of 241,776 documents used in this study. The most frequent words in the documents to appear cough, symptoms, tests, confirmed patients, mask and etc. Twenty topics (COVID-test, Economy, School, Hospital/Diagnose, Travel/Overseas, Health, Social issue, Symptom 1 (respiratory), Relationships, Symptom 2 (e.g., fever), Workplace, Mask/Social distancing, infection/Vaccine, Stimulus Package, Family, Delivery Service, Unclassified, Region, Study/Exam, Worry, Anxiety) were extracted using the topic modeling. There was a positive association between the daily counts of COVID-19 patients and proportion of negative sentiment. By COVID-19 period, Stage 4 had the highest correlation.

Conclusion:

This study identified the South Korean public’s interest and emotions about COVID-19 during the prolonged pandemic crisis. (Ewha Med J 2022;45(2):46-54)

Citations

Citations to this article as recorded by  
  • Identifying adverse reactions following COVID-19 vaccination in Korea using data from active surveillance: a text mining approach
    Hye Ah Lee, Bomi Park, Chung Ho Kim, Yeonjae Kim, Hyunjin Park, Seunghee Jun, Hyelim Lee, Seunghyun Lewis Kwon, Yesul Heo, Hyungmin Lee, Hyesook Park
    Epidemiology and Health.2025; 47: e2025034.     CrossRef
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