Identifying unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting approach-Reference-Cited by-同舟云学术

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Identifying unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting approach

Published:2021-01 Issue: Volume:92 Page:102658
ISSN:0278-4319
Container-title:International Journal of Hospitality Management
language:en
Short-container-title:International Journal of Hospitality Management

Author:

Zheng Tianxiang,Wu Feiran,Law Rob^ORCID,Qiu Qihang,Wu Rong

Funder

Special Funds of High-level University Construction Program of Guangdong Province

Publisher

Elsevier BV

Subject

Strategy and Management,Tourism, Leisure and Hospitality Management

Reference49 articles.

1. Sentiment analysis in tourism: capitalizing on big data;Alaei;J. Travel. Res.,2019

2. Hotel online reviews: creating a multi-source aggregated index;Antonio;Int. J. Contemp. Hosp. Manage.,2018

3. No unbiased estimator of the variance of k-fold cross-validation;Bengio;J. Mach. Learn. Res.,2004

4. Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews;Berezina;J. Hosp. Mark. Manage.,2016

5. Sentiment classification of consumer-generated online reviews using topic modeling;Calheiros;J. Hosp. Mark. Manage.,2017

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