Yelp Reviews and Food Types: A Comparative Analysis of Ratings, Sentiments, and Topics

TL;DR

This paper analyzes Yelp reviews to understand how ratings, sentiments, and review topics vary across different food types, revealing patterns and clusters that reflect user behavior and cultural influences.

Contribution

It introduces a comprehensive analysis of review ratings, sentiments, and topics across food types, with clustering and topic inference methods to uncover distinct patterns.

Findings

Identified four clusters of food types based on ratings and sentiments.

Found that review topics vary significantly among different food types.

Revealed cultural and behavioral influences on review patterns.

Abstract

This study examines the relationship between Yelp reviews and food types, investigating how ratings, sentiments, and topics vary across different types of food. Specifically, we analyze how ratings and sentiments of reviews vary across food types, cluster food types based on ratings and sentiments, infer review topics using machine learning models, and compare topic distributions among different food types. Our analyses reveal that some food types have similar ratings, sentiments, and topics distributions, while others have distinct patterns. We identify four clusters of food types based on ratings and sentiments and find that reviewers tend to focus on different topics when reviewing certain food types. These findings have important implications for understanding user behavior and cultural influence on digital media platforms and promoting cross-cultural understanding and appreciation.