Inferring Thematic Places from Spatially Referenced Natural Language Descriptions
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Places are more than just a location and spatial footprint. A sense of place is the result of subjective experience that a person has from being in a place or from interacting with information about a place. Although it is difficult to directly model a person’s conceptualization of sense of place in a computational representation, there exist many natural language data online that describe people’s experiences with places and which can be used to learn computational representations. In this paper we evaluate the usage of topic modeling on a set of travel blog entries to identify the themes that are most closely associated with places around the world. Using these representations we can calculate the similarity of places. In addition, by focusing on individual or sets of topics we identify new regions where topics are most salient. Finally we discuss how temporal changes in sense of place can be evaluated using these methods.
KeywordsTopic Modeling Latent Dirichlet Allocation Volunteer Geographic Information Topic Distribution Latent Dirichlet Allocation Model
The authors wish to thank Mike Goodchild and two anonymous reviewers for their valuable comments.
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