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

Ku, Chih Hao
Chang, Yung-Chun
Wang, Yichuan
Chen, Chien-Hung
Hsiao, Shih-Hui
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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.
Augmenting Human Intelligence: Artificially, Socially, and Ethically, Knowledge Innovation and Entrepreneurial Systems, Artificial Intelligence, Deep Learning, Hospitality Management, Visual Analytics
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