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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 likedoi:10.1080/22797254.2019.1586451 fatcat:akjw4aiazzezhdhez7unle627i