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

Justin Ku; Yung-Chun Chang; Yichung Wang; Chien-Hung Chen; Shih-Hui Hsiao

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

Justin Ku
, Yung-Chun Chang
, Yichung Wang
, Chien-Hung Chen
, Shih-Hui Hsiao

Information Systems

Lawrence Technological University
Taipei Medical University
Newcastle University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 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 language	English
Title of host publication	Proceedings of the Annual Hawaii International Conference on System Sciences
Editors	Tung X. Bui
Place of Publication	usa
Publisher	IEEE Computer Society
Pages	5268-5277
Number of pages	10
Volume	2019-January
ISBN (Electronic)	9780998133126
State	Published - Jan 1 2019
Event	52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 - Maui, United States Duration: Jan 8 2019 → Jan 11 2019

Conference

Conference	52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
Country/Territory	United States
Period	01/8/19 → 01/11/19

Cite this

@inproceedings{bfbe08d1c8e54a97a9f071f5bf9a2010,

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 = "Justin Ku and Yung-Chun Chang and Yichung Wang and Chien-Hung Chen and Shih-Hui Hsiao",

year = "2019",

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booktitle = "Proceedings of the Annual Hawaii International Conference on System Sciences",

publisher = "IEEE Computer Society",

note = "52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 ; Conference date: 08-01-2019 Through 11-01-2019",

}

Ku, J, Chang, Y-C, Wang, Y, Chen, C-H & Hsiao, S-H 2019, Artificial intelligence and visual analytics: A deep-learning approach to analyze hotel reviews & responses. in TX Bui (ed.), Proceedings of the Annual Hawaii International Conference on System Sciences. vol. 2019-January, IEEE Computer Society, usa, pp. 5268-5277, 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019, United States, 01/8/19.

Artificial intelligence and visual analytics: A deep-learning approach to analyze hotel reviews & responses. / Ku, Justin; Chang, Yung-Chun; Wang, Yichung et al.
Proceedings of the Annual Hawaii International Conference on System Sciences. ed. / Tung X. Bui. Vol. 2019-January usa: IEEE Computer Society, 2019. p. 5268-5277.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Artificial intelligence and visual analytics: A deep-learning approach to analyze hotel reviews & responses

AU - Ku, Justin

AU - Chang, Yung-Chun

AU - Wang, Yichung

AU - Chen, Chien-Hung

AU - Hsiao, Shih-Hui

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - Proceedings of the Annual Hawaii International Conference on System Sciences

A2 - Bui, Tung X.

PB - IEEE Computer Society

CY - usa

T2 - 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019

Y2 - 8 January 2019 through 11 January 2019

ER -

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

Abstract

Conference

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