Exploring Insurance and Natural Disaster Tweets Using Text Analytics

Tylor Huizinga, Anteneh Ayanso, Miranda Smoor and Ted Wronski
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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

International Journal of Business Analytics (IJBAN), 2017, vol. 4, issue 1, 1-17

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.

Date: 2017
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