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http://dx.doi.org/10.7319/kogsis.2015.23.2.089

Spatial Clustering Analysis based on Text Mining of Location-Based Social Media Data  

Park, Woo Jin (Center of Environmental Remediation and Risk Assessment, Seoul National University)
Yu, Ki Yun (Department of Civil & Environmental Engineering, Seoul National University)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.23, no.2, 2015 , pp. 89-96 More about this Journal
Abstract
Location-based social media data have high potential to be used in various area such as big data, location based services and so on. In this study, we applied a series of analysis methodology to figure out how the important keywords in location-based social media are spatially distributed by analyzing text information. For this purpose, we collected tweet data with geo-tag in Gangnam district and its environs in Seoul for a month of August 2013. From this tweet data, principle keywords are extracted. Among these, keywords of three categories such as food, entertainment and work and study are selected and classified by category. The spatial clustering is conducted to the tweet data which contains keywords in each category. Clusters of each category are compared with buildings and benchmark POIs in the same position. As a result of comparison, clusters of food category showed high consistency with commercial areas of large scale. Clusters of entertainment category corresponded with theaters and sports complex. Clusters of work and study showed high consistency with areas where private institutes and office buildings are concentrated.
Keywords
Social Media; Geo-tag; Text Mining; Spatial Distribution; Clustering Analysis;
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