COVID-19 and media dataset: Mining textual data according periods and countries (UK, Spain, France)

Abstract

These datasets contain a set of news articles in English, French and Spanish extracted from Medisys (i.e. advanced search) according the following criteria: (1) Keywords (at least): COVID-19, ncov2019, cov2019, coronavirus; (2) Keywords (all words): masque (French), mask (English), mascara (Spanish) (3) Periods: March 2020, May 2020, July 2020; (4) Countries: UK (English), Spain (Spanish), France (French). A corpus by country has been manually collected (copy/paste) from Medisys. For each country, 100 snippets by period (the 1st, 10th, 15th, 20th for each month) are built. The datasets are composed of: (1) A corpus preprocessed for the BioTex tool - https://gitlab.irstea.fr/jacques.fize/biotex_python (*.txt) [~ 900 texts]; (2) The same corpus preprocessed for the Weka tool - https://www.cs.waikato.ac.nz/ml/weka/ (*.arff); (3) Terms extracted with BioTex according spatio-temporal criteria (*.csv) [~ 9000 terms]. Other corpora can be collected with this same method. The code in Perl in order to preprocess textual data for terminology extraction (with BioTex) and classification (with Weka) tasks is available. A new version of this dataset (December 2020) includes additional data: - Python preprocessing and BioTex code [Execution_BioTex.tgz]. - Terms extracted with different ranking measures (i.e. C-Value, F-TFIDF-C_M) and methods (i.e. extraction of words and multi-word terms) with the online version of BioTex [Terminology_with_BioTex_online_dec2020.tgz],