The Text Mining of Public Policy Documents in Response to COVID-19: A Comparison of the United Arab Emirates and the Kingdom of Saudi Arabia
The Text Mining of Public Policy Documents in Response to COVID-19: A Comparison of the United Arab Emirates and the Kingdom of Saudi Arabia

Summary/Abstract: Emirates (UAE) and the Kingdom of Saudi Arabia (KSA) in order to identify key topics and themes for these twocountries in relation to the COVID-19 response.Research Design & Methods: In view of the availability of large volumes of documents as well as advancementin computing system, text mining has emerged as a significant tool to analyse large volumes of unstructured data.For this paper, we have applied latent semantic analysis and Singular Value Decomposition (SVD) for text clustering.Findings: The results of the analysis of terms indicate similarities of key themes around health and pandemic for the UAEand the KSA. However, the results of text clustering indicate that focus of the UAE’ documents in on ‘Digital’-relatedterms, whereas for the KSA, it is around ‘International Travel’-related terms. Further analysis of topic modellingdemonstrates that topics such as ‘Vaccine Trial’, ‘Economic Recovery’, ‘Health Ministry’, and ‘Digital Platforms’ arecommon across both the UAE and the KSA.Contribution / Value Added: The study contributes to text-mining literature by providing a framework for analyzingpublic policy documents at the country level. This can help to understand the key themes in policies of the governmentsand can potentially aid the identification of the success and failure of various policy measures in certain cases by meansof comparing the outcomes.Implications / Recommendations: The results of this study clearly showed that text clustering of unstructured data suchas policy documents could be very useful for understanding the themes and orientation topics of the policies.Keywords: text mining, COVID-19, public policy, information extraction, topic modelling, text clusteringArticle classification: research paperJEL classification: D78, E61, I18, L38

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