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. These efforts will ensure prevention of loss of life and reduce possible damages. The United States has been battered by multiple major hurricanes in the recent years and multiple avenues of disaster response efforts were being tested. Hurricane Irma can be recognized as the most popular hurricane in terms of social media attention. Irma made landfall in Florida as a Category 4 storm and preparation measures taken were intensive thus providing a good measure to evaluate in terms of efficacy. The effectiveness of the response methods utilized are evaluated using Twitter data that was collected from September 1st to September 16th, 2017. About 221,598 geotagged tweets were analyzed using sentiment analysis, text visualization, and exploratory analysis. The objective of this research is to establish an observable pattern regarding sentiment trends over the progression of the storm and produce a viable set of topic models for its totality. The study contributed to the literature by identifying which topics and keywords were most frequently used in tweets through sentiment analysis and topic modeling to determine what resources or concerns were most significant within a region during the hurricane Irma. The results from this study demonstrate that the sentiment analysis can measure people’s emotions during the natural disaster, which the authorities can use to limit the damage and effectively recover from the disaster. In this work, we have also reviewed the related works from the text/sentiment analysis, social media analysis from hurricanes/disaster perspective. This research can be further improved by incorporating sentiment analysis methods for classifying emoticons and non-textual components such as videos or images

A linguistically-driven methodology for detecting impending and unfolding emergencies from social media messages

Natural disasters have demonstrated the crucial role of social media before, during and after emergencies (Haddow & Haddow 2013). Within our EU project Sland \ub4 ail, we aim to ethically improve \ub4 the use of social media in enhancing the response of disaster-related agen-cies. To this end, we have collected corpora of social and formal media to study newsroom communication of emergency management organisations in English and Italian. Currently, emergency management agencies in English-speaking countries use social media in different measure and different degrees, whereas Italian National Protezione Civile only uses Twitter at the moment. Our method is developed with a view to identifying communicative strategies and detecting sentiment in order to distinguish warnings from actual disasters and major from minor disasters. Our linguistic analysis uses humans to classify alert/warning messages or emer-gency response and mitigation ones based on the terminology used and the sentiment expressed. Results of linguistic analysis are then used to train an application by tagging messages and detecting disaster- and/or emergency-related terminology and emotive language to simulate human rating and forward information to an emergency management system