Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change

《Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change》

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作者: Alexander W. Olson;Fernando Calderón-Figueroa;Olimpia Bidian;Daniel Silver;Scott Sanner

关键字: Computational social science;Urban informatics;Neighbourhood analysis;Machine learning;Text embedding

作者单位: Department of Mechanical and Industrial Engineering, Canada;Department of Sociology, Canada;University of Toronto, Canada;Department of Mechanical and Industrial Engineering, Canada;Department of Sociology, Canada;University of Toronto, Canada

摘要: This paper develops novel methods for using Yelp reviews as a window into the collective representations of a city and its neighbourhoods. Basing analysis on social media data such as Yelp is a challenging task because review data is highly sparse and direct analysis may fail to uncover hidden trends. To this end, we propose a deep autoencoder approach for embedding the language of neighbourhood-based business reviews into a reduced dimensional space that facilitates similarity comparison of neighbourhoods and their change over time. Our model improves performance in distinguishing real and fake neighbourhood descriptions derived from real reviews, increasing performance in the task from an average accuracy of 0.46 to 0.77. This improvement in performance indicates that this novel application of embedded language analysis permits us to uncover comparative trends in neighbourhood change through the lens of their venues' reviews, providing a computational methodology for reading a city through its neighbourhoods. The resulting toolkit makes it possible to examine a city's current sociological trends in terms of its neighbourhoods' collective identities.