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Exploring Insurance and Natural Disaster Tweets Using Text Analytics

Author

Listed:
  • Tylor Huizinga

    (Brock University, St. Catharines, Canada)

  • Anteneh Ayanso

    (Brock University, St. Catharines, Canada)

  • Miranda Smoor

    (Brock University, St. Catharines, Canada)

  • Ted Wronski

    (Brock University, St. Catharines, Canada)

Abstract

This study explores twitter data about insurance and natural disasters to gain business insights using text analytics. The program R was used to obtain tweets that included the word ‘insurance' in combination with other natural disaster words (e.g., snow, ice, flood, etc.). Tweets related to six top Canadian insurance companies as well as the top five insurance companies from the rest of the world, including the new entrant Google Insurance, was collected for this study. A total of 11,495 natural disaster tweets and 19,318 insurance company tweets were analyzed using association rule mining. The authors' analysis identified several strong rules that have implications for insurance products and services. These findings show the potential text mining applications offer for insurance companies in designing their products and services.

Suggested Citation

  • Tylor Huizinga & Anteneh Ayanso & Miranda Smoor & Ted Wronski, 2017. "Exploring Insurance and Natural Disaster Tweets Using Text Analytics," International Journal of Business Analytics (IJBAN), IGI Global, vol. 4(1), pages 1-17, January.
    • Handle: RePEc:igg:jban00:v:4:y:2017:i:1:p:1-17
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