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

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 hote...

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Autores principales:	Yu-Ming Chang, Chieh-Huang Chen, Jung-Pin Lai, Ying-Lei Lin, Ping-Feng Pai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sentiment analysis online reviews long short-term memory networks forecast hotel room occupancy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/184d48a5ffda42b8b3d31c3d90b4c231
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spelling	oai:doaj.org-article:184d48a5ffda42b8b3d31c3d90b4c2312021-11-11T15:19:39ZForecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews10.3390/app1121102912076-3417https://doaj.org/article/184d48a5ffda42b8b3d31c3d90b4c2312021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10291https://doaj.org/toc/2076-3417For 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.Yu-Ming ChangChieh-Huang ChenJung-Pin LaiYing-Lei LinPing-Feng PaiMDPI AGarticlesentiment analysisonline reviewslong short-term memory networksforecasthotel room occupancyTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10291, p 10291 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sentiment analysis online reviews long short-term memory networks forecast hotel room occupancy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	sentiment analysis online reviews long short-term memory networks forecast hotel room occupancy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yu-Ming Chang Chieh-Huang Chen Jung-Pin Lai Ying-Lei Lin Ping-Feng Pai Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews
description	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.
format	article
author	Yu-Ming Chang Chieh-Huang Chen Jung-Pin Lai Ying-Lei Lin Ping-Feng Pai
author_facet	Yu-Ming Chang Chieh-Huang Chen Jung-Pin Lai Ying-Lei Lin Ping-Feng Pai
author_sort	Yu-Ming Chang
title	Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews
title_short	Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews
title_full	Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews
title_fullStr	Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews
title_full_unstemmed	Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews
title_sort	forecasting hotel room occupancy using long short-term memory networks with sentiment analysis and scores of customer online reviews
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/184d48a5ffda42b8b3d31c3d90b4c231
work_keys_str_mv	AT yumingchang forecastinghotelroomoccupancyusinglongshorttermmemorynetworkswithsentimentanalysisandscoresofcustomeronlinereviews AT chiehhuangchen forecastinghotelroomoccupancyusinglongshorttermmemorynetworkswithsentimentanalysisandscoresofcustomeronlinereviews AT jungpinlai forecastinghotelroomoccupancyusinglongshorttermmemorynetworkswithsentimentanalysisandscoresofcustomeronlinereviews AT yingleilin forecastinghotelroomoccupancyusinglongshorttermmemorynetworkswithsentimentanalysisandscoresofcustomeronlinereviews AT pingfengpai forecastinghotelroomoccupancyusinglongshorttermmemorynetworkswithsentimentanalysisandscoresofcustomeronlinereviews
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Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews

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