Yelp Reviews and Food Types: A Comparative Analysis of Ratings,
  Sentiments, and Topics
Wenyu Liao Yiqing Shi Yujia Hu Wei Quan
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.