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

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.