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
DOI:
https://doi.org/10.15678/ZP.2021.55.1.02Keywords:
text mining, COVID-19, public policy, information extraction, topic modelling, text clusteringAbstract
Objective: The objective of the paper is to analyse publicly available government policy documents of the United Arab Emirates (UAE) and the Kingdom of Saudi Arabia (KSA) in order to identify key topics and themes for these two countries in relation to the COVID-19 response.
Research Design & Methods: In view of the availability of large volumes of documents as well as advancement in 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 UAE and the KSA. However, the results of text clustering indicate that focus of the UAE’ documents in on ‘Digital’-related terms, whereas for the KSA, it is around ‘International Travel’-related terms. Further analysis of topic modelling demonstrates that topics such as ‘Vaccine Trial’, ‘Economic Recovery’, ‘Health Ministry’, and ‘Digital Platforms’ are common across both the UAE and the KSA.
Contribution / Value Added: The study contributes to text-mining literature by providing a framework for analyzing public policy documents at the country level. This can help to understand the key themes in policies of the governments and can potentially aid the identification of the success and failure of various policy measures in certain cases by means of comparing the outcomes.
Implications / Recommendations: The results of this study clearly showed that text clustering of unstructured data such as policy documents could be very useful for understanding the themes and orientation topics of the policies.
Article classification: research paper
JEL classification: D78, E61, I18, L38
Downloads
Download data is not yet available.
References
Aburumman, A. (2020). The COVID-19 impact and survival strategy in business tourism market: The example of the UAE MICE industry. Humanities & Social Sciences Communications, available at: https://www.nature.com/articles/s41599-020-00630-8.
Alghamdi, R., & Alfalqi, K. (2015). A Survey of Topic Modeling in Text Mining. International Journal of Advanced Computer Science and Applications, 6(1), 147–153. https://doi.org/10.14569/ijacsa.2015.060121.
Al-Obeidat, F., Kafeza, E., & Spencer, B. (2018). Opinions Sandbox: Turning Emotions on Topics into Actionable Analytics. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and
Telecommunications Engineering, LNICST, 206, 110–119. https://doi.org/10.1007/978-3-319-67837-5_11.
Asmussen, C. B., & Møller, C. (2019). Smart literature review: A practical topi modelling approach to exploratory literature review. Journal of Big Data, 6(1), 91, https://doi.org/10.1186/s40537-019-0255-7.
Bechor, T., & Jung, B. (2019). Current State and Modeling of Research Topics in Cybersecurity and Data Science. Journal of Systemics, Cybernetics and Informatics, 17(1), 129–156.
Benedetto, F., & Tedeschi, A. (2016). Big data sentiment analysis for brand monitoring in social media streams by cloud computing. Studies in Computational Intelligence, 639, 341–377, https://doi.org/10.1007/978-3-319-30319-2_14.
Carracedo, P., Puertas Medina, R., & Luisa Martí Selva, M. (2020). Research lines on the impact of the COVID-19 pandemic on business. A text mining analysis. Journal of Business Research, 132, 586–593. https://doi.org/10.1016/j.jbusres.2020.11.043.
Cheng, X., Cao, Q., & Liao, S. S. (2020). An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation. Journal of Information Science, September 2020. https://doi.org/10.1177/0165551520954674.
CNN (2020). ‘Unprecedented’ Hajj begins – with 1,000 pilgrims, rather than the usual 2 million, https://edition.cnn.com/travel/article/hajj-2020-coronavirusintl/index.html
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407.
Goel, I., Sharma, S., & Kashiramka, S. (2021). Effects of the COVID-19 pandemic in India: An analysis of policy and technological interventions. Health Policy and Technology, 10(1), 151–164. https://doi.org/10.1016/j.hlpt.2020.12.001.
Gottipati, S., Shankararaman, V., & Lin, J. R. (2018). Text analytics approach to extract course improvement suggestions from students’ feedback. Research and Practice in Technology Enhanced Learning, RPTEL 13, 6.https://doi.org/10.1186/s41039-018-0073-0.
Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60–76. https://doi.org/10.4304/jetwi.1.1.60-76.
Hassounah, M., Rasheel, H. & Alhefzi, M. (2020). Digital Response During the COVID-19 Pandemic in Saudi Arabia. Journal of Medical Internet Research, 22(9), e19338.
He, W., Tian, X., Hung, A., Akula, V., & Zhang, W. (2018). Measuring and comparing service quality metrics through social media analytics: A case study. Information Systems and E-Business Management, 16(3), 579–600. https://doi.org/10.1007/s10257-017-0360-0.
Hofmann, T. (2001). Unsupervised learning by probabilistic Latent Semantic Analysis. Machine Learning, 42(1–2), 177–196. https://doi.org/10.1023/A:1007617005950.
KPMG Report for Saudi Arabia, November 2020, available at: https://home.kpmg/xx/en/home/insights/2020/04/saudi-arabia-government-andinstitution-measures-in-response-to-covid.html.
Madhoushi, Z., Hamdan, A. R., & Zainudin, S. (2015). Sentiment analysis techniques in recent works. Proceedings of the 2015 Science and Information Conference, SAI 2015, March, 288–291. https://doi.org/10.1109/SAI.2015.7237157.
Raja, A., Alshamsan A., & Al-Jedai, A. (2020). Current COVID-19 vaccine candidates: Implications in the Saudi population. Saudi Pharmaceutical Journal, 28(2020), 1743–1748.
Samuel, J., Ali, G. G. M. N., Rahman, M. M., Esawi, E., & Samuel, Y. (2020). COVID-19 public sentiment insights and machine learning for tweets classification. Information (Switzerland), 11(6), 1–22. https://doi.org/10.3390/info11060314.
Sarkar, D., Bali, R., Sharma, T., Sarkar, D., Bali, R., & Sharma, T. (2018). Analyzing Movie Reviews Sentiment. Practical Machine Learning with Python. https://doi.org/10.1007/978-1-4842-3207-1_7.
Sebei, H., Hadj Taieb, M. A., & Ben Aouicha, M. (2018). Review of social media analytics process and Big Data pipeline. Social Network Analysis and Mining, 8(1), 1–28. https://doi.org/10.1007/s13278-018-0507-0.
Sharma, A., Adhikary, A., & Bikash, S. (2020). Covid-19’s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data. Journal of Business Research, 117(May), 443–449. https://doi.org/10.1016/j.jbusres.2020.05.035.
Shi, L., Tsai, J., & Kao, S. (2009). Public health, social determinants of health, and public policy. Journal of Medical Science, 29(2), 43–59.
United Nations COVID-19 Socio-Economic Analysis for the United Arab Emirates, UN Report, September 2020.
Walker, R. M., Chandra, Y., Zhang, J., & van Witteloostuijn, A. (2019). Topic Modeling the Research-Practice Gap in Public Administration. Public Administration Review, 79(6), 931–937. https://doi.org/10.1111/puar.13095.
Wesslen, R. (2018). Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond. ArXiv.
Xu, J., Tao, Y., Yan, Y., & Lin, H. (2018). VAUT: A visual analytics system of spatiotemporal urban topics in reviews. Journal of Visualization, 21(3), 471–484. https://doi.org/10.1007/s12650-017-0464-0.
Yi, S., & Liu, X. (2020). Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review. Complex & Intelligent Systems, 6(3), 621–634. https://doi.org/10.1007/s40747-020-00155-240747-020-00155-2
Appendix
Various government entities and media reports citing government actions as the COVID-19 response have been taken from the following websites:
• UAE Embassy https://www.uae-embassy.org/
• Federal Authority for Identity and Citizenship (ICA) https://smartservices.ica.gov.ae/
• Ministry of Foreign Affairs https://www.mofaic.gov.ae/
• News and Media: https://www.khaleejtimes.com/
• News and Media: https://english.alarabiya.net
• News and Media: https://gulfnews.com
• News and Media: https://www.meed.com/
• News and Media https://www.arabnews.com/
• News and Media https://www.reuters.com/
• Ministry of Health and Preventions: https://www.mohap.gov.ae/
• News and Media: http://wam.ae/
• News and Media: https://www.jdsupra.com/
• News and Media: https://www.aljazeera.com/
• National emergency crisis and disaster recovery: https://www.ncema.gov.ae/
• Privately owned security services company: https://www.garda.com/
• Community platform for real estate: https://bldgtmrw.com/
• News and Media: https://www.cnbc.com/
• The National Emergency Crisis and Disasters Management Authority’s platform: http://www.weqaya.ae/
• News and Media: https://www.caixinglobal.com/
• Ministry of Health KSA initiative: https://covid19awareness.sa
• News and Media: https://www.bbc.com/
• International Monetary Fund: https://www.imf.org/
• Ministry of Health KSA: https://www.moh.gov.sa/
• Saudi Arabia Monetary Authority: http://www.sama.gov.sa/
• News and Media: https://www.atlas-mag.net/
• News and Media: https://thearabweekly.com/
• The Saudi Data and Artificial Intelligence Authority: https://sdaia.gov.sa/
• Integrated encyclopedia: https://mhtwyat.com/
• Johns Hopkins Aramco healthcare: https://www.jhah.com/
• Saudi Press Agency: https://www.spa.gov.sa/
Alghamdi, R., & Alfalqi, K. (2015). A Survey of Topic Modeling in Text Mining. International Journal of Advanced Computer Science and Applications, 6(1), 147–153. https://doi.org/10.14569/ijacsa.2015.060121.
Al-Obeidat, F., Kafeza, E., & Spencer, B. (2018). Opinions Sandbox: Turning Emotions on Topics into Actionable Analytics. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and
Telecommunications Engineering, LNICST, 206, 110–119. https://doi.org/10.1007/978-3-319-67837-5_11.
Asmussen, C. B., & Møller, C. (2019). Smart literature review: A practical topi modelling approach to exploratory literature review. Journal of Big Data, 6(1), 91, https://doi.org/10.1186/s40537-019-0255-7.
Bechor, T., & Jung, B. (2019). Current State and Modeling of Research Topics in Cybersecurity and Data Science. Journal of Systemics, Cybernetics and Informatics, 17(1), 129–156.
Benedetto, F., & Tedeschi, A. (2016). Big data sentiment analysis for brand monitoring in social media streams by cloud computing. Studies in Computational Intelligence, 639, 341–377, https://doi.org/10.1007/978-3-319-30319-2_14.
Carracedo, P., Puertas Medina, R., & Luisa Martí Selva, M. (2020). Research lines on the impact of the COVID-19 pandemic on business. A text mining analysis. Journal of Business Research, 132, 586–593. https://doi.org/10.1016/j.jbusres.2020.11.043.
Cheng, X., Cao, Q., & Liao, S. S. (2020). An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation. Journal of Information Science, September 2020. https://doi.org/10.1177/0165551520954674.
CNN (2020). ‘Unprecedented’ Hajj begins – with 1,000 pilgrims, rather than the usual 2 million, https://edition.cnn.com/travel/article/hajj-2020-coronavirusintl/index.html
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407.
Goel, I., Sharma, S., & Kashiramka, S. (2021). Effects of the COVID-19 pandemic in India: An analysis of policy and technological interventions. Health Policy and Technology, 10(1), 151–164. https://doi.org/10.1016/j.hlpt.2020.12.001.
Gottipati, S., Shankararaman, V., & Lin, J. R. (2018). Text analytics approach to extract course improvement suggestions from students’ feedback. Research and Practice in Technology Enhanced Learning, RPTEL 13, 6.https://doi.org/10.1186/s41039-018-0073-0.
Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60–76. https://doi.org/10.4304/jetwi.1.1.60-76.
Hassounah, M., Rasheel, H. & Alhefzi, M. (2020). Digital Response During the COVID-19 Pandemic in Saudi Arabia. Journal of Medical Internet Research, 22(9), e19338.
He, W., Tian, X., Hung, A., Akula, V., & Zhang, W. (2018). Measuring and comparing service quality metrics through social media analytics: A case study. Information Systems and E-Business Management, 16(3), 579–600. https://doi.org/10.1007/s10257-017-0360-0.
Hofmann, T. (2001). Unsupervised learning by probabilistic Latent Semantic Analysis. Machine Learning, 42(1–2), 177–196. https://doi.org/10.1023/A:1007617005950.
KPMG Report for Saudi Arabia, November 2020, available at: https://home.kpmg/xx/en/home/insights/2020/04/saudi-arabia-government-andinstitution-measures-in-response-to-covid.html.
Madhoushi, Z., Hamdan, A. R., & Zainudin, S. (2015). Sentiment analysis techniques in recent works. Proceedings of the 2015 Science and Information Conference, SAI 2015, March, 288–291. https://doi.org/10.1109/SAI.2015.7237157.
Raja, A., Alshamsan A., & Al-Jedai, A. (2020). Current COVID-19 vaccine candidates: Implications in the Saudi population. Saudi Pharmaceutical Journal, 28(2020), 1743–1748.
Samuel, J., Ali, G. G. M. N., Rahman, M. M., Esawi, E., & Samuel, Y. (2020). COVID-19 public sentiment insights and machine learning for tweets classification. Information (Switzerland), 11(6), 1–22. https://doi.org/10.3390/info11060314.
Sarkar, D., Bali, R., Sharma, T., Sarkar, D., Bali, R., & Sharma, T. (2018). Analyzing Movie Reviews Sentiment. Practical Machine Learning with Python. https://doi.org/10.1007/978-1-4842-3207-1_7.
Sebei, H., Hadj Taieb, M. A., & Ben Aouicha, M. (2018). Review of social media analytics process and Big Data pipeline. Social Network Analysis and Mining, 8(1), 1–28. https://doi.org/10.1007/s13278-018-0507-0.
Sharma, A., Adhikary, A., & Bikash, S. (2020). Covid-19’s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data. Journal of Business Research, 117(May), 443–449. https://doi.org/10.1016/j.jbusres.2020.05.035.
Shi, L., Tsai, J., & Kao, S. (2009). Public health, social determinants of health, and public policy. Journal of Medical Science, 29(2), 43–59.
United Nations COVID-19 Socio-Economic Analysis for the United Arab Emirates, UN Report, September 2020.
Walker, R. M., Chandra, Y., Zhang, J., & van Witteloostuijn, A. (2019). Topic Modeling the Research-Practice Gap in Public Administration. Public Administration Review, 79(6), 931–937. https://doi.org/10.1111/puar.13095.
Wesslen, R. (2018). Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond. ArXiv.
Xu, J., Tao, Y., Yan, Y., & Lin, H. (2018). VAUT: A visual analytics system of spatiotemporal urban topics in reviews. Journal of Visualization, 21(3), 471–484. https://doi.org/10.1007/s12650-017-0464-0.
Yi, S., & Liu, X. (2020). Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review. Complex & Intelligent Systems, 6(3), 621–634. https://doi.org/10.1007/s40747-020-00155-240747-020-00155-2
Appendix
Various government entities and media reports citing government actions as the COVID-19 response have been taken from the following websites:
• UAE Embassy https://www.uae-embassy.org/
• Federal Authority for Identity and Citizenship (ICA) https://smartservices.ica.gov.ae/
• Ministry of Foreign Affairs https://www.mofaic.gov.ae/
• News and Media: https://www.khaleejtimes.com/
• News and Media: https://english.alarabiya.net
• News and Media: https://gulfnews.com
• News and Media: https://www.meed.com/
• News and Media https://www.arabnews.com/
• News and Media https://www.reuters.com/
• Ministry of Health and Preventions: https://www.mohap.gov.ae/
• News and Media: http://wam.ae/
• News and Media: https://www.jdsupra.com/
• News and Media: https://www.aljazeera.com/
• National emergency crisis and disaster recovery: https://www.ncema.gov.ae/
• Privately owned security services company: https://www.garda.com/
• Community platform for real estate: https://bldgtmrw.com/
• News and Media: https://www.cnbc.com/
• The National Emergency Crisis and Disasters Management Authority’s platform: http://www.weqaya.ae/
• News and Media: https://www.caixinglobal.com/
• Ministry of Health KSA initiative: https://covid19awareness.sa
• News and Media: https://www.bbc.com/
• International Monetary Fund: https://www.imf.org/
• Ministry of Health KSA: https://www.moh.gov.sa/
• Saudi Arabia Monetary Authority: http://www.sama.gov.sa/
• News and Media: https://www.atlas-mag.net/
• News and Media: https://thearabweekly.com/
• The Saudi Data and Artificial Intelligence Authority: https://sdaia.gov.sa/
• Integrated encyclopedia: https://mhtwyat.com/
• Johns Hopkins Aramco healthcare: https://www.jhah.com/
• Saudi Press Agency: https://www.spa.gov.sa/
Downloads
Published
2021-12-21
How to Cite
Dwivedi, D. N., & Anand, A. (2021). 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. Journal of Public Governance, 55(1), 8–22. https://doi.org/10.15678/ZP.2021.55.1.02
Issue
Section
Articles
License
Open Access, licence: CC-BY 4.0
