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Identifying critical outbreak time window of controversial events based on sentiment analysis.
Wang, Mingyang; Wu, Huan; Zhang, Tianyu; Zhu, Shengqing.
  • Wang M; College of Information and Computer Engineering, Northeast Forestry University, Harbin, People's Republic of China.
  • Wu H; College of Information and Computer Engineering, Northeast Forestry University, Harbin, People's Republic of China.
  • Zhang T; College of Information and Computer Engineering, Northeast Forestry University, Harbin, People's Republic of China.
  • Zhu S; Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, People's Republic of China.
PLoS One ; 15(10): e0241355, 2020.
Article in English | MEDLINE | ID: covidwho-928216
ABSTRACT
The response of netizens toward controversial events plays an important guiding role in the development of events. Based on the identification of such responses, this study aimed to determine the critical outbreak time window of events. The microblog texts related to an event were divided into seven emotional categories via multi-emotional analysis to capture the subtle emotions of netizens toward an event, i.e., public opinion. By detecting the characteristics of the text and regional coverage of emotions, an emotional coverage index that reflects the intensity of emotional impact was proposed to determine the mainstream emotion of netizens. By capturing the mutation characteristics of the impact intensity of mainstream emotions, the critical time window of the public opinion toward the event was obtained. The experimental results demonstrated that the proposed method can effectively identify the critical outbreak time window of controversial events, which can help authorities in preventing the further aggravation of events.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Public Opinion / Social Media / Models, Theoretical Type of study: Randomized controlled trials Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Public Opinion / Social Media / Models, Theoretical Type of study: Randomized controlled trials Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2020 Document Type: Article