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

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  • Olson, Alex

Abstract

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.

Suggested Citation

  • Olson, Alex, 2020. "Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change," SocArXiv 8jbvg_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:8jbvg_v1
    DOI: 10.31219/osf.io/8jbvg_v1
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    1. Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2019. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 249-273, National Bureau of Economic Research, Inc.
    2. Elizabeth C. Delmelle, 2016. "Mapping the DNA of Urban Neighborhoods: Clustering Longitudinal Sequences of Neighborhood Socioeconomic Change," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(1), pages 36-56, January.
    3. Balázs Kovács & Glenn R. Carroll & David W. Lehman, 2014. "Authenticity and Consumer Value Ratings: Empirical Tests from the Restaurant Domain," Organization Science, INFORMS, vol. 25(2), pages 458-478, April.
    4. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    5. Daniel Arribas-Bel & Jessie Bakens, 2019. "Use and validation of location-based services in urban research: An example with Dutch restaurants," Urban Studies, Urban Studies Journal Limited, vol. 56(5), pages 868-884, April.
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