  In recent years, we have been faced with a series of natural disasters
causing a tremendous amount of financial, environmental, and human losses. The
unpredictable nature of natural disasters' behavior makes it hard to have a
comprehensive situational awareness (SA) to support disaster management. Using
opinion surveys is a traditional approach to analyze public concerns during
natural disasters; however, this approach is limited, expensive, and
time-consuming. Luckily the advent of social media has provided scholars with
an alternative means of analyzing public concerns. Social media enable users
(people) to freely communicate their opinions and disperse information
regarding current events including natural disasters. This research emphasizes
the value of social media analysis and proposes an analytical framework:
Twitter Situational Awareness (TwiSA). This framework uses text mining methods
including sentiment analysis and topic modeling to create a better SA for
disaster preparedness, response, and recovery. TwiSA has also effectively
deployed on a large number of tweets and tracks the negative concerns of people
during the 2015 South Carolina flood.
