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COVID-19疫苗的文献计量分析与情感分析

A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis.

作者信息

Sarirete Akila

机构信息

Computer Science Department College of Engineering, Effat University, Energy and Technology Research Center, Effat University Jeddah, Saudi Arabia.

出版信息

Procedia Comput Sci. 2021;194:280-287. doi: 10.1016/j.procs.2021.10.083. Epub 2021 Dec 3.

Abstract

Recent statistical and social studies have shown that social media platforms such as Instagram, Facebook, and Twitter contain valuable data that influence human behaviors. This data can be used to track, fight, and control the spread of the COVID-19 and are an excellent asset for analyzing and understanding people's sentiments. Current levels of willingness to receive a COVID-19 vaccination are still insufficient to achieve immunity standards as stipulated by the World Health Organization (WHO). The present study employs bibliometric analysis to uncover trends and research into sentiment analysis and COVID-19 vaccination. A range of analyses is conducted using the open-source tool VOSviewer and Scopus database from 2020-2021 to acquire a deeper insight and evaluate current research trends on COVID-19 vaccines. The quantitative methodology used generates various bibliometric network visualizations and trends as a function of publication metrics such as citation, geographical attributes, journal publications, and research institutions. Results of network visualization revealed that understanding the the-state-of-the-art in applying sentiment analysis to the COVID-19 pandemic is crucial to local government health agencies and healthcare providers to help in neutralizing the infodemic and improve vaccine acceptance.

摘要

最近的统计和社会研究表明,Instagram、Facebook和Twitter等社交媒体平台包含影响人类行为的宝贵数据。这些数据可用于追踪、抗击和控制新冠病毒的传播,是分析和理解人们情绪的绝佳资产。目前接受新冠疫苗接种的意愿水平仍不足以达到世界卫生组织(WHO)规定的免疫标准。本研究采用文献计量分析来揭示情绪分析和新冠疫苗接种方面的趋势及研究情况。使用开源工具VOSviewer和Scopus数据库对2020年至2021年的数据进行了一系列分析,以更深入地了解并评估当前关于新冠疫苗的研究趋势。所采用的定量方法根据诸如引用、地理属性、期刊出版物和研究机构等出版指标生成各种文献计量网络可视化结果和趋势。网络可视化结果显示,了解将情绪分析应用于新冠疫情的最新情况对于地方政府卫生机构和医疗服务提供者至关重要,有助于消除信息疫情并提高疫苗接种率。

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Over a decade of social opinion mining: a systematic review.十多年的社会舆论挖掘:一项系统综述。
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Research trends in COVID-19 vaccine: a bibliometric analysis.COVID-19 疫苗研究趋势:文献计量分析。
Hum Vaccin Immunother. 2021 Aug 3;17(8):2367-2372. doi: 10.1080/21645515.2021.1886806. Epub 2021 Mar 9.
3
5
A global survey of potential acceptance of a COVID-19 vaccine.一项针对 COVID-19 疫苗潜在接受度的全球调查。
Nat Med. 2021 Feb;27(2):225-228. doi: 10.1038/s41591-020-1124-9. Epub 2020 Oct 20.
7
Developing Covid-19 Vaccines at Pandemic Speed.以大流行速度研发新冠疫苗。
N Engl J Med. 2020 May 21;382(21):1969-1973. doi: 10.1056/NEJMp2005630. Epub 2020 Mar 30.
8
WHO Declares COVID-19 a Pandemic.世界卫生组织宣布新冠疫情为大流行病。
Acta Biomed. 2020 Mar 19;91(1):157-160. doi: 10.23750/abm.v91i1.9397.
9
The 100 top-cited studies on vaccine: a bibliometric analysis.100 篇高引疫苗研究论文:文献计量分析。
Hum Vaccin Immunother. 2019;15(12):3024-3031. doi: 10.1080/21645515.2019.1614398. Epub 2019 Jul 26.

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