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Detecting fake reviews with supervised machine learning algorithms

Published:2022-03-25 Issue:13-14 Volume:42 Page:1101-1121
ISSN:0264-2069
Container-title:The Service Industries Journal
language:en
Short-container-title:The Service Industries Journal

Author:

Lee Minwoo¹^ORCID,Song Young Ho²^ORCID,Li Lin³^ORCID,Lee Kyung Young⁴^ORCID,Yang Sung-Byung⁵^ORCID

Affiliation:

1. Conrad N. Hilton College of Hotel and Restaurant Management, University of Houston, Houston, TX, USA

2. Odette School of Business, University of Windsor, Windsor, Ontario, Canada

3. Business School, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

4. Rowe School of Business, Dalhousie University, Halifax, Nova Scotia, Canada

5. School of Management, Kyung Hee University, Seoul, Republic of Korea

Funder

Ministry of Education of the Republic of Korea

National Research Foundation of Korea

Publisher

Informa UK Limited

Subject

Management of Technology and Innovation,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/02642069.2022.2054996

Reference93 articles.

1. Linguistic Features for Detecting Fake Reviews

2. Support vector machines combined with feature selection for breast cancer diagnosis

3. Predictors and outcomes of consumer deception in hotel reviews: The roles of reviewer type and attribution of service failure

4. Customer perception of the deceptiveness of online product reviews: A speech act theory perspective

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3. Sentiment Analysis and Authenticity Assessment of Hotel Reviews;2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE);2024-01-24

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5. SGAN-SAM-ERNIE: A Novel and Effective Detection Scheme for Chinese Fake Reviews;IEEE Access;2024

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