Yelp Reviews and Food Types: A Comparative Analysis of Ratings, Sentiments, and Topics
It provides insights into user behavior and cultural influences on digital platforms, but the work is incremental as it applies existing methods to new data without major innovations.
This study analyzed Yelp reviews to compare ratings, sentiments, and topics across different food types, revealing that some food types share similar patterns while others are distinct, with four clusters identified based on ratings and sentiments.
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.