Geocoding of Spatial Relationships Contained in Tweets

Human ability to understand approximate references to locations, disambiguated by means of context and reasoning about spatial relationships, is the key to describe spatial environments and to share information about them. In this paper, the authors propose an approach for geocoding that takes advantage of the spatial relationships contained in the text of tweets, using semantic and spatial analyses. Microblog text has special characteristics (e.g. slang, abbreviations, acronyms, etc.) and thus represents a special variation of natural language. The main objective of this work is to associate spatial relationships found in text with a spatial footprint, to determine the location of the event described in the tweet. The feasibility of the proposal is demostrated using a corpus of 200,000 tweets posted in Spanish related with traffic events in Mexico City.