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.

本研究研究了Yelp评论与食品类别之间的关系，调查了各种食品类型的评分、情感和主题在不同食品类型之间的变化。通过分析评论的评分和情感如何在食品类型之间变化，基于评分和情感对食品类型进行聚类，采用机器学习模型推测评论主题，并比较不同食品类型之间的主题分布。我们的分析发现，某些食品类型的评分、情感和主题分布相似，而其他食品类型则具有明显的模式。我们基于评分和情感确定了四个食品类型的聚类，并发现评论者在评论某些食品类型时倾向于关注不同的主题。这些发现对于理解用户行为和文化对数字媒体平台的影响以及促进跨文化理解和欣赏具有重要意义。

Yelp评论和食物类型: 评分、情感和主题的比较分析