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Identification of Natural Disaster Affected Area Using Twitter

  • Satish Muppidi
  • P. Srinivasa Rao
  • M. Rama Krishna MurthyEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

Any social network activity can be posted now a days in Twitter. People reach out to twitter during natural disasters for help by tweeting the areas that are affected with the natural disaster and the type of natural disaster that has occurred. As, Social media is greatly relied at the times of natural disasters, this makes it very important that there must be an efficient method to analyze the disaster related tweets and find out the largely affected areas by the natural disaster. In this paper we classify the natural disaster-based tweets from the users using classification machine algorithms like Naïve Bayes, Logistic Regression, KNN, Random Forest and determine the best machine learning algorithm (based on metrics like accuracy, kappa etc.) that can be relied to ascertain the severity of the natural disaster at a desired area.

Keywords

Sentimental analysis Machine learning algorithms Twitter Natural disasters 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Satish Muppidi
    • 1
  • P. Srinivasa Rao
    • 2
  • M. Rama Krishna Murthy
    • 3
    Email author
  1. 1.Department of Information TechnologyGMRITRajamIndia
  2. 2.Department of CSEMVGRCEVizianagaramIndia
  3. 3.Department of CSEANITSVisakhapatnamIndia

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