Artificial Intelligence and Visual Analytics: A Deep-Learning Approach to Analyze Hotel Reviews & Responses

Chih-Hao Ku, Yung-Chun Chang, Yichuan Wang, Chien-Hung Chen, Shih-Hui Hsiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With a growing number of online reviews, consumers often rely on these reviews to make purchase decisions. However, little is known about managerial responses to online hotel reviews. This paper reports on a framework to integrate visual analytics and machine learning techniques to investigate whether hotel managers respond to positive and negative reviews differently and how to use a deep-learning approach to prioritize responses. In this study, forty 4- and 5-star hotels in London with 91,051 reviews and 70,397 responses were collected and analyzed. Visual analyses and machine learning were conducted. The results indicate most hotels (72.5%) showing no preference to respond to positive and negative reviews. Our proposed deep-learning approach outperformed existing algorithms to prioritize responses. © 2019 IEEE Computer Society. All rights reserved.
Original languageEnglish
Title of host publicationProceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
PublisherIEEE Computer Society
Pages5238-5277
Publication statusPublished - Jan 8 2019
Event52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 - Maui, United States
Duration: Jan 8 2019Jan 11 2019

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2019-January
ISSN (Print)1530-1605

Conference

Conference52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
Country/TerritoryUnited States
CityMaui
Period1/8/191/11/19

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