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arXiv · 1501.02527

Autodetection and Classification of Hidden Cultural City Districts from Yelp Reviews

Harini Suresh·Nicholas Locascio
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Abstract

Topic models are a way to discover underlying themes in an otherwise unstructured collection of documents. In this study, we specifically used the Latent Dirichlet Allocation (LDA) topic model on a dataset of Yelp reviews to classify restaurants based off of their reviews. Furthermore, we hypothesize that within a city, restaurants can be grouped into similar "clusters" based on both location and similarity. We used several different clustering methods, including K-means Clustering and a Probabilistic Mixture Model, in order to uncover and classify districts, both well-known and hidden (i.e. cultural areas like Chinatown or hearsay like "the best street for Italian restaurants") within a city. We use these models to display and label different clusters on a map. We also introduce a topic similarity heatmap that displays the similarity distribution in a city to a new restaurant.

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Harini Suresh, Nicholas Locascio. 2015-01-12. Autodetection and Classification of Hidden Cultural City Districts from Yelp Reviews. https://arxiv.org/abs/1501.02527

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