In recent years, social media has become ubiquitous and important for social networking and content sharing. Especially, in the disaster area, social media supports backchannel communications, allowing for wide-scale interaction that can be collectively resourceful, self-policing, and generative of information that is otherwise hard to obtain. At the time of great Japan and Haiti earthquake, social media channels actively were utilizing to grasp the damage, to warn, and to exchange information. This paper is to introduce the model for sensing the signs of inundation and detecting inundation risk areas by analyzing big data related to disasters. This model is comprised of three steps: a sign sensing step through monitoring and analyzing unstructured data such as social media, a risk detecting step through comparing and analyzing structured data such as precipitation, inundation hazard maps, and so forth, and a disaster status dissemination step to disaster related organizations, local governments, and the public. By applying our model to Gangnam inundation damage, 2011, in Korea, we substantiated that there is the potential for utilization on our model.

People use social media platforms such as Twitter to record their personal thoughts and opinions. Social media platforms reflect people's sentiments as they are, and an accurate understanding of sentiments on social media could be useful and significant for disaster management. In this research, sentiment type modeling and sentiment quantification are proposed to understand the sentiments presented on social media platforms. Sentiment types are primarily analyzed based on the three major sentiments of affirmation, caution, and observation. Then, for a detailed understanding of sentiment progress according to the progress of a disaster or accident and the government's response, negative sentiments are categorized into anxiety, disappointment, depression, sadness, and displeasure to enhance the analysis, while positive sentiments are categorized into pleasure, happiness, and relief; Russell's circumplex model is used to develop a model of eight primary sentiments to acquire an overall understanding of the public's sentiments. Then, the sentiment index of each sentiment is quantified. Based on the results, the overall sentiment status of the public is monitored, and in the event of a disaster, the public's sentiment fluctuation rate can be quantitatively observed. Moreover, the influence of disasters and accidents on public sentiments, or the sentiment indices of different accidents, can be compared to identify the accidents that affect public sentiment and public needs after a disaster, and the insights can be used for policy-making.