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Sentiment Analysis of the Covid-19 Vaccines on Social Media.
Melton, Chad A; Olusanya, Olufunto A; Ammar, Nariman; Shaban-Nejad, Arash.
  • Melton CA; Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, United States.
  • Olusanya OA; University of Tennessee Health Science Center-Oak-Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, United States.
  • Ammar N; University of Tennessee Health Science Center-Oak-Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, United States.
  • Shaban-Nejad A; University of Tennessee Health Science Center-Oak-Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, United States.
Stud Health Technol Inform ; 290: 1056-1057, 2022 Jun 06.
Article in English | MEDLINE | ID: covidwho-1933593
ABSTRACT
The COVID-19 pandemic fueled one of the quickest vaccine developments in history. Misinformation on online social media often leads to negative vaccine sentiment. We conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling from Reddit communities focusing on the COVID-19 vaccine. Polarity analysis suggested these communities expressed positive sentiment regarding the vaccine. However, topic modeling revealed community members mainly focused on the side effects and vaccination experience.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / Social Media / COVID-19 Type of study: Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220265

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / Social Media / COVID-19 Type of study: Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220265