A VISUAL EXPERIMENT ON POLICY DATA DURING COVID-19
By FATMA GUNERI
A text mining study has been realised on a sample policy « Income support to quarantined workers who cannot work from home » published by OECD on 24 july 2020, in the dataset « Policy responses to the Covid-19 crisis ». Policy texts have been analysed and descriptive word frequency resolutions produced, followed by data visualisation. The objective is to show how visualisation of texts might clarify messages by making them simpler. The sample country for this study was Australia. The totality of this country’s policies is explained by 1172 words. And the sample policy chosen for this study was 135 words. However, the word cloud created by this policy was including 40 words. This decrease in the number of words saves reading time and makes the main theme of the text quickly identifiable. Furthermore, the visualization of themes might accelerate the connection between different subjects and the creation of interpretation. During stressful periods like Covid-19 people look for quick, short information. Social media becomes a preference because of its easy access. If institutions prepare visualized reports with text mining techniques, people might be more interested in their policies. The visualisations by Tagcrowd and Infranodus:
FIGURES
https://www.oecd.org/social/Covid-19-Employment-and-Social-Policy-Responses-by-Country.xlsx
https://infranodus.com/
https://tagcrowd.com/
https://en.wikipedia.org/wiki/Australia
Attachment: a_visual_experiment_guneri.pdf (326 KB)
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