Yelp Dataset Analysis using Scalable Big Data

TL;DR

This paper analyzes the Yelp dataset to uncover patterns in business performance, user behavior, and temporal trends, revealing declines in reviews and check-ins over time and regional differences in ratings.

Contribution

It provides a comprehensive descriptive analysis of the Yelp dataset, highlighting temporal and regional trends in user reviews, ratings, and check-ins.

Findings

Yelp reviews, tips, elite users, and check-ins have decreased over the years.

Canadian ratings are more stable than American ratings.

Temporal and regional patterns in user engagement are identified.

Abstract

Yelp has served and will continue to serve as a data-driven application. Yelp has published a dataset containing business information, reviews, user information, and check-in information. This paper will examine this dataset to provide descriptive analytics to understand business performance, geo-spatial distribution of businesses, reviewers' rating and other characteristics, and temporal distribution of check-ins in business premises. With these analysis we are able to establish that yelp reviews, tips, elite users and check ins have started to plummet over the years. Coincidentally, the paper also establishes that Canadians have a more stable star ratings as well as sentiment ratings when compared to Americans.