DISASTER SENTIMENT CLASSIFICATION BASED ON BIG DATA MEANING AND METHOD FOR PREVENTING DISASTER BY USING SAME

摘要

The present invention relates to disaster sentiment classification based on big data meaning and to a method for preventing a disaster by using the same. The disaster sentiment classification method based on big data meaning according to the present invention is a disaster sentiment classification method that can monitor how emotions felt by people change depending on the occurrence process of each disaster type or a response plan. If a disaster occurs, the disaster becomes more serious with the lapse of time and accordingly a response is demanded. Moreover, if the disaster is tackled by the demand, response satisfaction and response dissatisfaction are generated. The disaster sentiment is classified into five types of emotions: anxiety, seriousness, sadness, dissatisfaction and affirmation based on the meaning of big data such as sensors, social media and media reports in each process of the disaster from the occurrence of the disaster. The sentiment classification based on big data meaning and the method for preventing a disaster by using the same according to the present invention, as configured above, can provide a technique for detecting the precursor of a disaster and propagating a response via the fusion analysis of disaster big data to solve the problems of a traditional technique and to comply with social demands by developing a disaster monitoring technique based on big data, and developing and conducting a disaster response propagating technique via the fusion analysis of typical and atypical big data.;COPYRIGHT KIPO 2016