Geo-spatial text-mining from Twitter - a feature space analysis with a view toward building classification in urban regions

Abstract

By the year 2050, it is expected that about 68% of global population will live in cities. To understand the emerging changes in urban structures, new data sources like social media must be taken into account. In this work, we conduct a feature space analysis of geo-tagged Twitter text messages from the Los Angeles area and a geo-spatial text mining approach to classify buildings types into commercial and residential. To create the feature space, broadly accepted word embedding models like word2vec, fastText and GloVe as well as more traditional models based on TF-IDF have been considered. A visual analysis of the word embeddings shows that the two examined classes yield several word clusters. However, the classification results produced by Naïve Bayes support vector machines, and a convolutional neural network indicates that building classification from pure social media text is quite challenging. Furthermore, this work illustrates a base toward fusing text features and remote sensing images to classify urban building types.