Big Data, Sentiment Analysis, and Examples during the COVID-19 Pandemic

PDF
Published: Dec 29, 2022
DOI: https://doi.org/10.12681/hapscpbs.33779
Keywords:
Big Data Spatial Big Data Sentiment Analysis COVID – 19
Kyvele Constantina Diareme
Agricultural University of Athens, Department of Agricultural Economics and Development, Informatics Laboratory; New York College, Athens, Informatics Department, Greece
Anastasios Liapakis
National and Kapodistrian University of Athens, Department of Digital Industry Technologies, Greece
Iris Efthymiou
University of Piraeus, Laboratory of Health Economics and Management, Greece
Abstract

Applied research in Big Data has gained popularity and is already transforming corporations, public sector, health care and subsequently everyday life. Big Data are being analysed for a variety of reasons, e.g., predict Brexit negotiating outcomes, optimise operations in agriculture, map and analyse human mobility trends under non-pharmaceutical interventions during the recent pandemic. The period of the COVID-19 pandemic has been characterised also by an ‘infodemic’, meaning an overabundance of both good and bad information. This information needs to be managed effectively as it can yield valuable insights when analysed. In this paper the terms of Big Data, Geospatial Big Data and Sentiment Analysis are presented along with selected cases, from the international literature, of the use of Big Data and analytics during the COVID-19 pandemic.

Article Details
  • Section
  • Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Downloads
Download data is not yet available.
References
Alaoui, I. el, & Gahi, Y. (2019). The impact of big data quality on sentiment analysis approaches. Procedia Computer Science, 160: 803–810.
Alsunaidi, S. J., Almuhaideb, A. M., Ibrahim, N. M., Shaikh, F. S., Alqudaihi, K. S., Alhaidari, F. A., Khan, I. U., Aslam, N., & Alshahrani, M. S. (2021). Applications of big data analytics to control covid‐19 pandemic. In Sensors 21(7). MDPI AG.
Azzaoui, A. E. L., Singh, S. K., & Park, J. H. (2021). SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Healthy City. Sustainable Cities and Society, 71(March), 102993.
Barbaglia, L., Frattarolo, L., Onorante, L., Pericoli, F. M., Ratto, M., & Pezzoli, L. T. (2022). Testing big data in a big crisis: Nowcasting under COVID-19. International Journal of Forecasting.
Cobb, A. N., Benjamin, A. J., Huang, E. S., & Kuo, P. C. (2018). Big data: More than big data sets. Surgery, 164(4): 640–642.
Chong, A. Y. L., Li, B., Ngai, E. W., Ch'ng, E., & Lee, F. (2016). Predicting online product sales via online reviews, sentiments, and promotion strategies: A big data architecture and neural network approach. International Journal of Operations & Production Management.
Diareme K.C., Tsiligiridis Th. (2018). Big Data for Personalised Agricultural Touring Recommendations, In: Proceedings of 7th International Symposium and 29th National Conference on Operational Research - The contribution of Operational Research, new technologies and innovation in agriculture and tourism, 9-14, June 14-16, 2018, Chania, Crete, Greece.
Efthymiou, I.-P., Sidiropoulos, S., Kritas, D., Rapti, P., Vozikis, A., & Souliotis, K. (2020a). AI transforming Healthcare Management during Covid-19 pandemic. HAPSc Policy Briefs Series, 1(1): 130-138.
Efthymiou, I. P., Psomiadi, A., Diareme, K. C., & Chatzivasileiou, S. (2020b). Using AI Changes the Paradigm of Women's Participation in Politics. HAPSc Policy Briefs Series, 1(2): 26-34.
European Investment Bank (2021). INVESTMENT REPORT 2021/2022.
Ferrara, L., Mogliani, M., & Sahuc, J. G. (2022). High-frequency monitoring of growth at risk. International Journal of Forecasting, 38(2): 582–595.
Galetsi, P., Katsaliaki, K., & Kumar, S. (2022). The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19. In: Social Science and Medicine (Vol. 301). Elsevier Ltd.
Georgiadou, E., Angelopoulos, S., & Drake, H. (2020). Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes. International Journal of Information Management, 51.
Haafza, L. A., Awan, M. J., Abid, A., Yasin, A., Nobanee, H., & Farooq, M. S. (2021). Big data covid-19 systematic literature review: Pandemic crisis. In: Electronics (Switzerland)10(24).MDPI.
Hu, S., Xiong, C., Yang, M., Younes, H., Luo, W., & Zhang, L. (2021). A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic. Transportation Research Part C: Emerging Technologies, 124(December 2020), 102955.
Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2019). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Industrial Marketing Management.
Lee, J. G., & Kang, M. (2015). Geospatial Big Data: Challenges and Opportunities. Big Data Research, 2(2): 74–81.
Leung, C. K., Chen, Y., Shang, S., & Deng, D. (2020). Big Data Science on COVID-19 Data. Proceedings - 2020 IEEE 14th International Conference on Big Data Science and Engineering, BigDataSE 2020, 14–21.
Liapakis A., Diareme K.C., Yialouris C., Costopoulou C., Tsiligiridis Th., Gorou P., (2022). Natural Language Processing Tools for performing effective text mining tasks in Greek Food & Beverage Sector. 10th International Conference on ICT in Agriculture, Food & Environment September 22-25, 2022, Athens, Greece
Liapakis, A., Tsiligiridis, T., Yialouris, C., & Maliappis, M. (2020). A Corpus Driven, Aspect-based Sentiment Analysis To Evaluate In Almost Real-time, A Large Volume of Online Food & Beverage Reviews. International Journal of Computational Linguistics (IJCL), 11(2): 49-60.
Liapakis, A., Tsiligiridis, T., & Yialouris, C. (2020). Design of a Sentiment Lexicon for the Greek Food and Beverage Sector. Operational Research in Agriculture and Tourism: 49-66.
Liapakis, A., Tsiligiridis, T., & Yialouris, C. (2020). A Sentiment Lexicon-based Analysis for Food and Beverage Industry Reviews. The Greek Language Paradigm. International Journal of Natural Language Computing.
Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., & Cheng, T. (2016). Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 115(2016): 119–133.
Li, X., Zhao, Z., & Liu, F. (2020). Big data assimilation to improve the predictability of COVID-19. Geography and Sustainability, 1(4): 317–320.
Lu, Y., Zhao, J., Wu, X., & Lo, S. M. (2021). Escaping to nature during a pandemic: A natural experiment in Asian cities during the COVID-19 pandemic with big social media data. Science of the Total Environment, 777.
Mackey TK, Li J, Purushothaman V, Nali M, Shah N, Bardier C, Cai M, Liang B. Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram. JMIR Public Health Surveill. 25;6(3):e20794.
Mantelero, A. (2018). AI and Big Data: A blueprint for a human rights, social and ethical impact assessment. Computer Law and Security Review, 34(4): 754–772.
Mark, R. (2019). Ethics of Using AI and Big Data in Agriculture: The Case of a Large Agriculture Multinational. The ORBIT Journal, 2(2): 1–27.
Melenli, S., & Topkaya, A. (2020). Real-Time Maintaining of Social Distance in Covid-19 Environment using Image Processing and Big Data. Proceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020.
Mir, S. A., Bhat, M. S., Rather, G. M., & Mattoo, D. (2021). Role of big geospatial data in the COVID-19 crisis. In: Data Science for COVID-19: Volume 2: Societal and Medical Perspectives. Elsevier, 589–609
Peddireddy, A. S., Xie, D., Patil, P., Wilson, M. L., MacHi, D., Venkatramanan, S., Klahn, B., Porebski, P., Bhattacharya, P., Dumbre, S., Raymond, E., & Marathe, M. (2020). From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, 1380–1387.
Piccarozzi, M., & Aquilani, B. (2022). The role of Big Data in the business challenge of Covid-19: A systematic literature review in managerial studies. Procedia Computer Science, 200: 1746–1755.
Qiu, H.-J., Yuan, L.-X., Wu, Q.-W., Zhou, Y.-Q., Zheng, R., Huang, X.-K., & Yang, Q.-T. (2020). Using the internet search data to investigate symptom characteristics of COVID-19: A big data study. World Journal of Otorhinolaryngology - Head and Neck Surgery, 6: S40–S48.
Qorib, M., Oladunni, T., Denis, M., Ososanya, E., & Cotae, P. (2022). Covid-19 Vaccine Hesitancy: Text Mining, Sentiment Analysis and Machine Learning on COVID-19 Vaccination Twitter Dataset. Expert Systems with Applications, 118715.
Rice, W., & Pan, B. (2020). Understanding drivers of change in park visitation during the COVID-19 pandemic: A spatial application of Big data. Wellbeing, Space and Society, 100037.
Riswantini, D., Nugraheni, E., Arisal, A., Khotimah, P. H., Munandar, D., & Suwarningsih, W. (2021). Big data research in fighting COVID-19: Contributions and techniques. Big Data and Cognitive Computing, 5(3).
Sanguinetti, M., Bosco, C., Cassidy, L., Çetinoğlu, Ö., Cignarella, A. T., Lynn, T., Rehbein, I., Ruppenhofer, J., Seddah, D., & Zeldes, A. (2022). Treebanking user-generated content: a UD based overview of guidelines, corpora and unified recommendations. Language Resources and Evaluation.
Shang, S., Shen, J., Wen, J. R., & Kalnis, P. (2021). Deep understanding of big geospatial data for self-driving cars. Neurocomputing, 428: 308–309.
Shang, W. L., Chen, J., Bi, H., Sui, Y., Chen, Y., & Yu, H. (2021). Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis. Applied Energy, 285: 116429.
Sharma, J., Sharma, D., & Sharma, K. (2021). Retail Analytics to anticipate Covid-19 effects Using Big Data Technologies. 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021.
Shi, F., Wei, Y., Xia, L., Shan, F., Mo, Z., Yan, F., & Shen, D. (2021). Lung volume reduction and infection localization revealed in Big data CT imaging of COVID-19. International Journal of Infectious Diseases, 102: 316–318.
Smirnova, E., Ivanescu, A., & Bai, J. (2018). A practical guide to big data. Statistics and Probability Letters: 1–5.
Tacconelli, E., Gorska, A., Carrara, E., Joanna Davis, R., Bonten, M., Friedrich, A. W., Glasner, C., Goossens, H., Hasenauer, J., Maria Haro Abad, J., Pe, J. L., Sanchez-Niubo, A., Sialm, A., Scipione, G., Soriano, G., Yazdanpanah, Y., Vorstenbosch, E., Jaenisch, T., & Sanitari Sant Joan de eu, P. D. (2022). Challenges of data sharing in European Covid-19 projects: A learning opportunity for advancing pandemic preparedness and response-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). The Lancet Regional Health - Europe, 21: 100467.
Xu, H., Liu, R., Luo, Z., & Xu, M. (2022). COVID-19 vaccine sensing: Sentiment analysis and subject distillation from twitter data. Telematics and Informatics Reports, 8: 100016.
Zhan, J., Dong, S., & Hu, W. (2022). IoE-supported smart logistics network communication with optimization and security. Sustainable Energy Technologies and Assessments, 52.
Zhang, Q., Gao, B., & Luqman, A. (2022). Linking green supply chain management practices with competitiveness during covid 19: The role of big data analytics. Technology in Society, 70.
Zhang, Q., Gao, J., Wu, J. T., Cao, Z., & Dajun Zeng, D. (2022). Data science approaches to confronting the COVID-19 pandemic: A narrative review. In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Vol. 380, Issue 2214). Royal Society Publishing.
Zhang, Y., Li, Q., Wang, H., Du, X., & Huang, H. (2019). Community scale livability evaluation integrating remote sensing, surface observation and geospatial big data. International Journal of Applied Earth Observation and Geoinformation, 80(20): 173–186.
Zhou, S., Zhou, S., Zheng, Z., Lu, J., & Song, T. (2022). Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China. Applied Geography, 143.
World Health Organization (2021). Infodemic management: an overview of infodemic management during COVID-19, January 2020–May 2021.