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Review Article

Towards Data Analytics Approach for Monitoring of Disaster Management using Machine Learning Techniques: A Systematic Literature Review (SLR)

  • Hassan Adamu1 ✉
  • Auwalu Saleh2
  • Marzuq Musa3
  • Sabo Muhammad4
  • Sa’ad Barau Gambasha5
  • Abdullahi Adamu Ahmad6
  • A. A. Isah7
  1. 1Department of Computer Science, Binyaminu Usman Polytechnic Hadejia
  2. 2Department of Computer Science, Binyaminu Usman Polytechnic Hadejia
  3. 3Department of Computer Science, Binyaminu Usman Polytechnic Hadejia
  4. 4Department of Computer Science, College of Education Gumel
  5. 5Department of Computer Science, Binyaminu Usman Polytechnic Hadejia
  6. 6PPM Unit, Kano Electricity Distribution Company
  7. 7Department of Computer Science, Binyaminu Usman Polytechnic Hadejia

Abstract

Opinion mining is not just applicable to businesses, but also to other areas such as politics and governance. As of April 2020, there are 4.57 billion active internet users and 3.81 billion active social media users depositing huge amount of data on several social media platform such as Facebook, Twitter, Instagram, WhatsApp and others. During the period of disaster response, a large number of users post several information on Twitter like disaster damage reports and disaster preparedness situations, making Twitter an essential social media for updating and accessing data. Globally, governments spend huge amounts to provide aid packages to their citizens. The primary objective of government palliatives, relief and aid packages is to cushion economic and psychological effects on the citizens affected by disaster. This study aims to conduct a systematic literature review (SLR) to identify the current researches concerning the public opinion and sentiment on Twitter with regard to COVID-19 palliative and relief aid packages distributed to vulnerable citizens during disaster. Also, to find the most effective machine learning algorithms in classifying public opinion and sentiment on Twitter social media by extracting data from two digital libraries (databases) Scopus and ProQuest. Conclusively, Findings from the SLR study shows that Naïve Bayes and Support Vector Machine (SVM) classifiers are the most popular and effective techniques used on Twitter opinion and sentiment analysis

Keywords

How to cite

Adamu, H., Saleh, A., Musa, M., Muhammad, S., Gambasha, S. B., Ahmad, A. A., & Isah, A. A. (2022). Towards Data Analytics Approach for Monitoring of Disaster Management using Machine Learning Techniques: A Systematic Literature Review (SLR). Semi-Arid Journal of Academic Research and Development, 5(1), 72–84.

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