| 输入 | The unprecedented outbreak of COVID-19 is one of the most serious global threats to public health in this century. During this crisis, specialists in information science could play key roles to support the efforts of scientists in the health and medical community for combatting COVID-19. In this article, we demonstrate that information specialists can support health and medical community by applying text mining technique with latent Dirichlet allocation procedure to perform an overview of a mass of coronavirus literature. This overview presents the generic research themes of the coronavirus diseases: COVID-19, MERS and SARS, reveals the representative literature per main research theme and displays a network visualisation to explore the overlapping, similarity and difference among these themes. The overview can help the health and medical communities to extract useful information and interrelationships from coronavirus-related studies. |
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1）如何作图？
1，准备作图数据；2，用excel打开数据，调整为示例格式；3，将调整后的数据粘贴到输入框；4，选择参数；5，提交出图
2）为什么不出图？
程序对输入格式有严格要求。请务必仔细查看右侧说明及示例数据
3）如何引用？
1300+篇google学术，~1000篇知网学术引用
请使用原生R包，Python包进行引用，或使用如下格式（推荐直接写网址）
Heatmap was plotted by https://www.bioinformatics.com.cn (last accessed on 5 Jun 2023), an online platform for data analysis and visualization.
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