Artificial intelligence and visual analytics: A deep-learning approach to analyze hotel reviews & responses

Chih Hao Ku, Yung Chun Chang, Yichung Wang, Chien Hung Chen, Shih Hui Hsiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages5268-5277
Number of pages10
ISBN (Electronic)9780998133126
StatePublished - 2019
Externally publishedYes
Event52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 - Maui, United States
Duration: Jan 8 2019Jan 11 2019

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2019-January
ISSN (Print)1530-1605

Conference

Conference52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
Country/TerritoryUnited States
CityMaui
Period1/8/191/11/19

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint

Dive into the research topics of 'Artificial intelligence and visual analytics: A deep-learning approach to analyze hotel reviews & responses'. Together they form a unique fingerprint.

Cite this