# Autodetection and Classification of Hidden Cultural City Districts from Yelp Reviews

arXiv: https://arxiv.org/abs/1501.02527 · HTML: https://mlsanity.com/paper/1501.02527/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2015.

## Built on by (most cited first, 1 of 1)

- [Using Graph Neural Networks to Predict Local Culture](https://mlsanity.com/paper/2402.17905/) (2024)

## Abstract (arXiv)

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.