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http://elar.urfu.ru/handle/10995/118139
Название: | COVID-19 outlook in the United States of America: A data-driven thematic approach |
Авторы: | Agbozo, E. Watat, J. K. Olaleye, S. A. |
Дата публикации: | 2021 |
Издатель: | Elsevier |
Библиографическое описание: | Agbozo E. COVID-19 outlook in the United States of America: A data-driven thematic approach / E. Agbozo, J. K. Watat, S. A. Olaleye // Data Science for COVID-19: Volume 2: Societal and Medical Perspectives. — 2021. — P. 77-93. |
Аннотация: | At the inception of the coronavirus disease 2019 (COVID-19) pandemic, different categories of planners have emerged around the globe, such as Proactive, Preactive, Inactive, and Reactive planners. They all fall into the groups of early birds and latecomers. America, as a benevolent country, is playing a central role in the ongoing battle against COVID-19. Despite America’s leading humanitarian and health assistance response to COVID-19, it did not exempt the country from the deadly coronavirus. Despite the controversies surrounding the virus, the American government has taken several steps to reduce the virus spread and flatten the mortality curve. Studies by the Centers for Disease Control and Prevention (CDC) share new knowledge and enlighten the public on the position of COVID-19 globally, but there is a vacuum in having a deeper understanding of the emerging themes in America concerning COVID-19. This research embarked on a thematic and sentiment analysis via text mining techniques from Twitter data to contribute to the ongoing academic discourse with respect to COVID-19 within the context of the United States of America. The results show relevant and unexpected bigrams. This study clarifies some uncertainty regarding the COVID-19 outlook in America. In addition, researchers can extend these results to other countries that have been dominated by COVID-19. Finally, this research discusses the limitations and gives future policy direction regarding COVID-19. © 2022 Elsevier Inc. |
Ключевые слова: | AMERICA COVID-19 DATA-DRIVEN THEMATIC ANALYSIS |
URI: | http://elar.urfu.ru/handle/10995/118139 |
Условия доступа: | info:eu-repo/semantics/openAccess |
Идентификатор РИНЦ: | 49068680 |
Идентификатор SCOPUS: | 85130173469 |
Идентификатор PURE: | 30207911 |
ISBN: | 9780323907699 9780323907705 |
DOI: | 10.1016/B978-0-323-90769-9.00008-6 |
Располагается в коллекциях: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
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Файл | Описание | Размер | Формат | |
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2-s2.0-85130173469.pdf | 1,71 MB | Adobe PDF | Просмотреть/Открыть |
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