Microblogs posted to Twitter after the tornado in Moore, Oklahoma, on May 20, 2013, were analyzed in this study. The potential of social media data was explored for the extraction of relevant and useful information during natural disasters and as an additional data source for better understanding of individual behavior during a crisis. Data records were attributed to user groups, and the most frequently used words were ranked to track the variation of common interests for each user group. In addition, the data were classified into different content categories, and the temporal variation patterns were analyzed. A sentiment analysis, which revealed variations in public mood and perception over time, was conducted to quantify the sentiment in the data. The techniques presented can be applied to the analysis of similar major social crises and natural disasters (e.g., hurricanes and earthquakes) to provide valuable complementary information in crisis awareness and response to users, first responders, and emergency preparedness agencies. Different stakeholders can determine the needs and activities of people during disasters by using the proposed method with the help of social media data.