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This study examines the relationship between Yelp reviews and food types,investigating how ratings, sentiments, and topics vary across different typesof food. Specifically, we analyze how ratings and sentiments of reviews varyacross food types, cluster food types based on ratings and sentiments, inferreview topics using machine learning models, and compare topic distributionsamong different food types. Our analyses reveal that some food types havesimilar ratings, sentiments, and topics distributions, while others havedistinct patterns. We identify four clusters of food types based on ratings andsentiments and find that reviewers tend to focus on different topics whenreviewing certain food types. These findings have important implications forunderstanding user behavior and cultural influence on digital media platformsand promoting cross-cultural understanding and appreciation.

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