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

Autodetection and Classification of Hidden Cultural City Districts from Yelp Reviews