Abstract
A lot of research has been done during the first months of 2020 regarding the Covid-19. Researchers of different fields worked and cooperated to understand the virus better, in order to manage the pandemic and to model its spread. A series of tools have been developed in this sense, but there is a lack of work with regards to what has been developed from the scientific community. We would like to, at least partially, summarise the results obtained so far by analysing some of the published papers on the matter. To achieve such a result, we are going to use different python libraries that allow analysing texts. The entire work has been done with python on the Google Colaboratory platform.
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Di Cicco Mattia Fonisto, F.: Covid-19 papers analysis
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Amato, A., Cozzolino, G., Maisto, A., Pelosi, S. (2021). Analysis of COVID-19 Data. In: Barolli, L., Takizawa, M., Yoshihisa, T., Amato, F., Ikeda, M. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2020. Lecture Notes in Networks and Systems, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-61105-7_25
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DOI: https://doi.org/10.1007/978-3-030-61105-7_25
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