Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews

Yu-Ming Chang,Chieh-Huang Chen,Ying-Lei Lin,Ping-Feng Pai,Jung-Pin Lai

doi:10.3390/app112110291

Abstract

For hotel management, occupancy is a crucial indicator. Online reviews from customers have gradually become the main reference for customers to evaluate accommodation choices. Thus, this study employed online customer rating scores and review text provided by booking systems to forecast monthly hotel occupancy using long short-term memory networks (LSTMs). Online customer reviews of hotels in Taiwan in various languages were gathered, and Google’s natural language application programming interface was used to convert online customer reviews into sentiment scores. Five other forecasting models—back propagation neural networks (BPNN), general regression neural networks (GRNN), least square support vector regression (LSSVR), random forest (RF), and gaussian process regression (GPR)—were employed to predict hotel occupancy using the same datasets. The numerical data indicated that the long short-term memory network model outperformed the other five models in terms of forecasting accuracy. Integrating hotel online customer review sentiment scores and customer rating scores can lead to more accurate results than using unique scores individually. The novelty and applicability of this study is the application of deep learning techniques in forecasting room occupancy rates in multilingual comment scenarios with data gathered from review text and customers’ rating scores. This study reveals that using long short-term memory networks with sentiment analysis of review text and customers’ rating scores is a feasible and promising alternative in forecasting hotel room occupancy.

Highlights

Business performance management is significant for enterprises in any industry, especially in the tourism and hotel industry, because of the fixed service products in terms of content and quantity
The Tourism Bureau of the Ministry of Transportation in Taiwan pointed out some statistical indicators ranking the operating performance of hotels, such as total revenue, annual occupancy rate, annual average price, and the annual average price of saleable rooms
The results indicated that a seasonal autoregressive integrated moving average with exogenous variables model was superior to the other forecasting models

Summary

Introduction

Business performance management is significant for enterprises in any industry, especially in the tourism and hotel industry, because of the fixed service products in terms of content and quantity. One category involves developing new methods to improve the accuracy of demand prediction This type of research usually employs many alternative forecasting models to predict travel or hotel demand and compares forecasting performance. More studies are using customer online comment analysis as the subject of hotel management research. The Tourism Bureau of the Ministry of Transportation in Taiwan pointed out some statistical indicators ranking the operating performance of hotels, such as total revenue, annual occupancy rate, annual average price, and the annual average price of saleable rooms. The second category involves employing data-driven approaches to explore the relationship between customers’ reviews and hotel performance. In the previous three categories of studies mentioned, descriptive statistics, hypothesis testing, correlation analysis, cluster analysis, and regression analysis were employed by studies to analyze the influence of customers’ online reviews on hotel performance. Predictions of hotel performance, such as room occupancy, have not been conducted

Occupancy Rate Forecast

Long Short-Term Memory Networks

The Proposed Hotel Occupancy Forecasting Architecture

Modeling and Testing

Findings

Conclusions