Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks

Methods

Volumn 129, Issue , 2017, Pages 50-59

  Choi, Sungwoon ^a,b   Lee, Jangho ^a   Kang, Min Gyu ^c   Min, Hyeyoung ^d   Chang, Yoon Seok ^e   Yoon, Sungroh ^a,f  

^a Seoul National University   (South Korea)

^b Korea University   (South Korea)

^c Chungbuk National University Hospital   (South Korea)

^d Chung Ang University   (South Korea)

^e Seoul National University Bundang Hospital   (South Korea)

^f STANFORD UNIVERSITY   (United States)

Author keywords

Machine learning; Middle East respiratory syndrome (MERS); Natural language processing; Sentiment analysis

Indexed keywords

ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; DATA PROCESSING; ECONOMIC ASPECT; EMOTION; EPIDEMIC; INTERNET; LANGUAGE PROCESSING; MACHINE LEARNING; MASS MEDIUM; MIDDLE EAST RESPIRATORY SYNDROME; PRIORITY JOURNAL; PUBLIC OPINION; SOCIAL BEHAVIOR; SOCIAL MEDIA; SOUTH KOREA; TEXT MESSAGING; CORONAVIRUS INFECTION; HUMAN; MIDDLE EAST RESPIRATORY SYNDROME CORONAVIRUS; PATHOGENICITY; VIROLOGY;

CORONAVIRUS INFECTIONS; DISEASE OUTBREAKS; HUMANS; MACHINE LEARNING; MASS MEDIA; MIDDLE EAST RESPIRATORY SYNDROME CORONAVIRUS; REPUBLIC OF KOREA;

EID: 85028462739     PISSN: 10462023     EISSN: 10959130     Source Type: Journal    
DOI: 10.1016/j.ymeth.2017.07.027     Document Type: Article

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