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Lookup NU author(s): Dr YiChuan WangORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Association of Information Systems (AIS), 2019.
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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.
Author(s): Ku CH, Chang YC, Wang Y, Chen CH, Hsiao SH
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 52nd Annual Hawaii International Conference on System Sciences (HICSS)
Year of Conference: 2019
Print publication date: 03/01/2019
Acceptance date: 04/09/2018
Date deposited: 08/09/2018
Publisher: Association of Information Systems (AIS)