Rating prediction of peer-to-peer accommodation through attributes and topics from customer review

Abstract

This study aims to predict customers’ behavior in classifying their reviews as high rated or low rated using associated attributes and topics found in the review. Knowing customer reviewing action better can lead to a successful strategy implementation of the relevant parties related to this study such as policy to manage customer reviews by keeping their satisfaction high. We applied a big data approach on a dataset of 55,377 reviews from Airbnb listings in the top 10 most visited cities in Indonesia (based on foreign arrivals data). We used The Classification and Regression Tree Model, Random Forest Model, Least Absolute Shrinkage and Selection Operation and Logistic Regression Model, Artificial Neural Network as well as Multi-Layer Perceptron to make prediction’s classification. Those models are used to identify a set of attributes and topics that will increase the chance of the review to render a high rate and a different set of attributes and topics that will lead the review to be low rated. This study found; first, attributes and topics that influence customers' odds to classify their review as high rated or low rated adhere to the understanding of Peer to Peer accommodation attributes. Second, successfully proved that customer reviews' attributes and topics could be used to predict the classification of ratings in Peer to Peer accommodation. Where for Topics, we can predict the rating using Random Forest yields 60.09% accuracy, slightly better than Artificial Neural Network (58.33%) and Multi-Layer Perceptron (58.8%). However, it seems better to use Attributes to predict the rating, where the accuracy is yielded better by applying Artificial Neural Network with 84.79% accuracy compared to Multi-Layer Perceptron with only 72.35% of accuracy.