An Evaluation of Geotagged Twitter Data during Hurricane Irma Using Sentiment Analysis and Topic Modeling for Disaster Resilience

Ike Vayansky, Sathish A.P. Kumar, Zhenlong Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Disasters require quick response times, thought-out preparations, overall community, and government support to ensure the prevention of loss of life and reduce possible damages. Hurricane Irma can be recognized as a more popular recent disaster in terms of social media attention and made landfall in the US with significant time to prepare, making it a good model for an evaluation of disaster response. The objective of this research is to establish a pattern regarding sentiment trends over the progression of the storm totality using sentiment analysis and produce a viable set of topic models for its and Latent Dirichlet Allocation (LDA) topic modeling. The results from this study demonstrate that sentiment analysis can measure changes in users's emotions during natural disasters and that simple topic models can be formed from the twitter data. Information like this can be used by authorities to limit the damage and effectively recover from the disaster as well as adjust future response efforts accordingly. This research can be further improved by incorporating sentiment analysis methods for short texts, classifying emoticons and non-Textual components such as videos or images, and optimizing data collection and preparation methods.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Technology and Society, ISTAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728154800
DOIs
StatePublished - Nov 2019
Event2019 IEEE International Symposium on Technology and Society, ISTAS 2019 - Boston, United States
Duration: Nov 15 2019Nov 16 2019

Publication series

NameInternational Symposium on Technology and Society, Proceedings
Volume2019-November

Conference

Conference2019 IEEE International Symposium on Technology and Society, ISTAS 2019
Country/TerritoryUnited States
CityBoston
Period11/15/1911/16/19

All Science Journal Classification (ASJC) codes

  • General Engineering
  • General Social Sciences

Cite this