@inproceedings{a3e8885d5db44851be129038fb571cfe,
title = "Artificial intelligence and visual analytics: A deep-learning approach to analyze hotel reviews & responses",
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.",
author = "Ku, {Chih Hao} and Chang, {Yung Chun} and Yichung Wang and Chen, {Chien Hung} and Hsiao, {Shih Hui}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE Computer Society. All rights reserved.; 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 ; Conference date: 08-01-2019 Through 11-01-2019",
year = "2019",
language = "English (US)",
series = "Proceedings of the Annual Hawaii International Conference on System Sciences",
publisher = "IEEE Computer Society",
pages = "5268--5277",
editor = "Bui, {Tung X.}",
booktitle = "Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019",
address = "United States",
}