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Characterizing and Predicting Yelp Users’ Behavior

  • Parvathy Jayaprakasan
  • R. N. UmaEmail author
  • A. Sankarasubramanian
Chapter
  • 1.3k Downloads
Part of the Studies in Big Data book series (SBD, volume 27)

Abstract

A business’ revenue is significantly dependent on Yelp user ratings (Luca, Reviews, reputation, and revenue: the case of Yelp.com. Harvard Business School Working Paper, No. 12-016, 2016; Anderson and Magruder J Econ J. 122(563):957–989, 2012). Knowing the characteristics of Yelp users will influence their business practices that would eventually help improve their Yelp ratings and consequently their revenue. We categorize Yelp users based on the average number of stars given by each user for their reviews. We determine the common characteristics and differences of users between these user groups; and determine whether these characteristics change by business category. We conclude that users whose average rating falls between 3.7 and 4.0 are the most influential and socially connected and that the type of business does not affect the characteristics of the users. Additionally, we design a two-stage predictive model to predict the average star rating of users given their features or attributes and compare its performance to standard models such as random forest and generalized additive model.

Keywords

Yelp Users Average Star Rating Generalized Additive Models (GAM) Business Category Random Forest Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Parvathy Jayaprakasan
    • 1
  • R. N. Uma
    • 1
    Email author
  • A. Sankarasubramanian
    • 2
  1. 1.Department of Mathematics and PhysicsNorth Carolina Central UniversityDurhamUSA
  2. 2.Department of Civil, Construction and Environmental EngineeringNorth Carolina State UniversityRaleighUSA

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