Debiasing Online Reviews with Machine Learning

Abstract

Online review aggregation platforms suffer from biases which can lead to distress to both the platforms and their consumers as the average rating crowd sourced on these platforms do not represent the correct perceived quality of the product or service. We look at the problem of polarization bias on Yelp and present the evaluation of an estimation model to determine the unbiased average rating. We explore how the Yelp's elite membership program helps in cutting down the bias. Our results propose that the average biased rating listed on platforms is correlated with the true unbiased rating and by including more information from online reviews as input features of our model we can get a reasonably well estimate of the average unbiased ratings. We propose and compare several predictive models to estimate unbiased average ratings and show how textual data can play a critical role in enhancing the predictive power. Our results can help review aggregation platforms to determine mechanisms to cut down the bias on their platforms and can benefit businesses and consumers on their platform to access a fairer metric for service quality.