MOOD project is at the forefront of European research of infectious disease surveillance and modelling from a data science perspective, investigating the impact of global warming on disease outbreaks, and proposing innovations for building of One Health systems across Europe and the world.
In the table below are listed all MOOD publications. Use the filter to select the most relevant articles.
1.
Roche, Mathieu
COVID-19 and Media datasets: Period-and location-specific textual data mining Journal Article
In: Data in brief, vol. 33, pp. 106356, 2020.
Abstract | Links | BibTeX | Tags: COVID-19, machine learning, media extraction, Text mining
@article{roche2020covid,
title = {COVID-19 and Media datasets: Period-and location-specific textual data mining},
author = {Mathieu Roche},
doi = {10.1016/j.dib.2020.106356},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Data in brief},
volume = {33},
pages = {106356},
publisher = {Elsevier},
abstract = {The vocabulary used in news on a disease such as COVID-19 changes according the period [4]. This aspect is discussed on the basis of MEDISYS-sourced media datasets via two studies. The first focuses on terminology extraction and the second on period prediction according to the textual content using machine learning approaches.},
keywords = {COVID-19, machine learning, media extraction, Text mining},
pubstate = {published},
tppubtype = {article}
}
The vocabulary used in news on a disease such as COVID-19 changes according the period [4]. This aspect is discussed on the basis of MEDISYS-sourced media datasets via two studies. The first focuses on terminology extraction and the second on period prediction according to the textual content using machine learning approaches.