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• W4309346031 abstract "COVID-19 created immense global challenges in 2020, and the world will live under its threat indefinitely. Much of the information on social media supported the government in addressing this major public health event. On January 9, to control the virus, the Chinese government announced universal vaccinations. However, due to a range of varied interpretations, people held different attitudes towards vaccination. Therefore, the success of the mass immunization strategy greatly depended on the public perception of the COVID-19 vaccine. This article explores the changes in people’s emotional attitudes towards vaccines and the reasons behind them in the context of the global pandemic in an effort to help mankind overcome this ongoing crisis. For this article, microblogs from January to September containing Chinese people’s responses to the COVID-19 vaccines were collected. Based on fuzzy logic and deep learning, we advance the hypothesis that fuzzy vector adaptive improvements will make it possible to better express language emotion and that fuzzy emotion vectors can be integrated into deep learning models, thus making these models more interpretable. Based on this assumption, we design a deep learning model with a fuzzy emotion vector. The experimental results show the positive effect of this model. By applying the model in analyses of people’s attitudes towards vaccines, we can obtain people’s attitudes towards vaccines in different time periods. We discovered that the most negative emotions about the vaccine appeared in April and that the most positive emotions about the vaccine appeared in February. Combined with word cloud technology and the LDA model, we can effectively explore the reasons for the changes in vaccine attitudes. Our findings show that people’s negative emotions about the vaccine are always higher than their positive emotions about the vaccine and that people’s attitudes towards the vaccine are closely related to the progress of the epidemic. There is also a certain relationship between people’s attitudes towards the vaccine and those towards the vaccination." @default.
• W4309346031 created "2022-11-26" @default.
• W4309346031 creator A5010738468 @default.
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• W4309346031 date "2022-11-16" @default.
• W4309346031 modified "2023-10-18" @default.
• W4309346031 title "Emotion Analysis of COVID-19 Vaccines Based on a Fuzzy Convolutional Neural Network" @default.
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• W4309346031 doi "https://doi.org/10.1007/s12559-022-10068-6" @default.
• W4309346031 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36406893" @default.
• W4309346031 hasPublicationYear "2022" @default.
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