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Building Type Classification from Social Media Texts via Geo-Spatial Textmining

In this work, we present a model for building type classification from Twitter text messages (tweets) by employing geo-spatial textmining methods. First, we apply standard text pre-processing methods and convert the tweets into sentence vectors using fastText. For classification, we apply a feedforward network with two fully connected hidden layers and feed the generated sentence vectors as linguistic features. Classification results suggest that the classes are distinguishable to a certain extent with pure text even with unbalanced class distributions and a very small sample size. However, these findings also undermine, that building type classification with pure text data is a challenging task.