Hotel Preference Rank based on Online Customer Review

TL;DR

This paper presents a machine learning approach using Bayesian classification and topic modeling to analyze online reviews, providing a rapid hotel ranking based on service quality dimensions from TripAdvisor reviews.

Contribution

It introduces a novel method combining Bayesian classifiers and topic modeling to evaluate hotel service quality from online reviews, enabling faster hotel ranking insights.

Findings

Responsiveness is the most common issue in reviews.

The model achieved high accuracy and F-measure in classification.

Online review analysis can effectively inform hotel ranking.

Abstract

Topline hotels are now shifting into the digital way in how they understand their customers to maintain and ensuring satisfaction. Rather than the conventional way which uses written reviews or interviews, the hotel is now heavily investing in Artificial Intelligence particularly Machine Learning solutions. Analysis of online customer reviews changes the way companies make decisions in a more effective way than using conventional analysis. The purpose of this research is to measure hotel service quality. The proposed approach emphasizes service quality dimensions reviews of the top-5 luxury hotel in Indonesia that appear on the online travel site TripAdvisor based on section Best of 2018. In this research, we use a model based on a simple Bayesian classifier to classify each customer review into one of the service quality dimensions. Our model was able to separate each classification properly by accuracy, kappa, recall, precision, and F-measure measurements. To uncover latent topics in the customer's opinion we use Topic Modeling. We found that the common issue that occurs is about responsiveness as it got the lowest percentage compared to others. Our research provides a faster outlook of hotel rank based on service quality to end customers based on a summary of the previous online review.