The Next Generation of Disaster Management and Relief Planning: Immersive Analytics Based Approach

  • Radhia ToujaniEmail author
  • Yasmin Chaabani
  • Zeineb Dhouioui
  • Hanen Bouali
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 840)


Managing the risks of natural disasters can be enhanced by exploring social data. The need to swiftly extract meaningful information from large amounts of data generated by social network is on the rise especially to deal with natural disasters. New methods are needed to deeply support immersive social data analytics. Moreover, big data analysis seems to be able to improve accurate decisions to disaster management systems. The aim of this research is to determine critical cases and to focus on immersive sentiment analysis for big social data using Hadoop platform and machine learning technique. In one hand, we use MapReduce for the introduced data processing step. In the other hand, we apply support vector machine algorithm for the sentiment classification. We evaluate the performance of the performed classification method using the standard classification performance metrics accuracy, precision, recall, and F-measure and Microsoft Power BI as a visualization tool.


Social network Social network analysis Sentiment analysis Sentiment classification Immersive classification Immersive analytics Big data MapReduce Machine learning Disaster management 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Radhia Toujani
    • 1
    Email author
  • Yasmin Chaabani
    • 1
  • Zeineb Dhouioui
    • 1
  • Hanen Bouali
    • 1
  1. 1.BESTMOD Department, Higher Institute of ManagementUniversity of TunisTunisTunisia

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