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With social media widely used for interpersonal communication, it has served as one important channel for information creation and propagation especially during hazard events. Users of social media in hazard-affected area can capture and upload hazard information more timely by portable and internet-connected electric devices such as smart phones or tablet computers equipped with (Global Positioning System) GPS devices and cameras. The information from social media(e.g. Twitter, facebook, sina-weibo, WebChat, etc.) contains a lot of hazard related information including texts, pictures, and videos. Most important thing is that a fair proportion of these crowd-sourcing information is valuable for the geospatial analysis in Geographic information system (GIS) during the hazard mitigation process. The geospatial information (position of observer, hazard-affected region, status of damages, etc) can be acquired and extracted from social media data. And hazard related information could also be used as the GIS attributes. But social media data obtained from crowd-sourcing is quite complex and fragmented on format or semantics. In this paper, we introduced the method how to acquire and extract fine-grained hazard damage geospatial information. According to the need of hazard relief, we classified the extracted information into eleven hazard loss categories and we also analyzed the public's sentiment to the hazard. The 2017 typhoon "Hato" was selected as the case study to test the method introduced.