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

dc.contributor.author Ku, Chih Hao
dc.contributor.author Chang, Yung-Chun
dc.contributor.author Wang, Yichuan
dc.contributor.author Chen, Chien-Hung
dc.contributor.author Hsiao, Shih-Hui
dc.date.accessioned 2019-01-03T00:36:13Z
dc.date.available 2019-01-03T00:36:13Z
dc.date.issued 2019-01-08
dc.description.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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2019.634
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59963
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Augmenting Human Intelligence: Artificially, Socially, and Ethically
dc.subject Knowledge Innovation and Entrepreneurial Systems
dc.subject Artificial Intelligence, Deep Learning, Hospitality Management, Visual Analytics
dc.title Artificial Intelligence and Visual Analytics: A Deep-Learning Approach to Analyze Hotel Reviews & Responses
dc.type Conference Paper
dc.type.dcmi Text
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