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.
|Name||Proceedings of the Annual Hawaii International Conference on System Sciences|
|Conference||52nd Annual Hawaii International Conference on System Sciences, HICSS 2019|
|Period||1/8/19 → 1/11/19|