Cargando…
Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on Tweets
OBJECTIVE: To develop and apply a natural language processing (NLP)-based approach to analyze public sentiments on social media and their geographic pattern in the United States toward coronavirus disease 2019 (COVID-19) vaccination. We also aim to provide insights to facilitate the understanding of...
Autores principales: | Ye, Jiancheng, Hai, Jiarui, Wang, Zidan, Wei, Chumei, Song, Jiacheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097455/ https://www.ncbi.nlm.nih.gov/pubmed/37063408 http://dx.doi.org/10.1093/jamiaopen/ooad023 |
Ejemplares similares
-
Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective
por: Rahman, Md Mahbubar, et al.
Publicado: (2022) -
Leveraging Tweets for Artificial Intelligence Driven Sentiment Analysis on the COVID-19 Pandemic
por: Alkhaldi, Nora A., et al.
Publicado: (2022) -
Analysing sentiment change detection of Covid-19 tweets
por: Theocharopoulos, Panagiotis C., et al.
Publicado: (2023) -
Social Networking Service, Patient-Generated Health Data, and Population Health Informatics: National Cross-sectional Study of Patterns and Implications of Leveraging Digital Technologies to Support Mental Health and Well-being
por: Ye, Jiancheng, et al.
Publicado: (2022) -
A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets
por: Kaur, Harleen, et al.
Publicado: (2021)