Identifying unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting approach |
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| Affiliation: | 1. Shenzhen Tourism College/JNU-UF International Joint Laboratory on Information Technology & Tourism, Jinan University, No.6, Qiaocheng East Avenue, Overseas Chinese Town, Nanshan District, Shenzhen, Guangdong, 518053, PR China;2. School of Hotel and Tourism Management, The Hong Kong Polytechnic University, 17 Science Museum Road, TST East, Kowloon, Hong Kong SAR, 999077, China;3. Faculty of Human Geography and Planning, Adam Mickiewicz University, Krygowskiego 10, 61-680 Poznan, Poland;4. School of Architecture and Urban Planning, Guangdong University of Technology, No.729, Dongfeng East Road, Yuexiu District, Guangzhou, Guangdong, 510090, PR China;1. School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong SAR, China;2. Kemmons Wilson School of Hospitality & Resort Management, The University of Memphis, Memphis, TN 38152, United States;3. School of Hospitality & Tourism Management, University of Surrey, Guildford, Surrey, GU2 7XH, UK;4. School of Hotel, Restaurant and Tourism Management, University of South Carolina, Columbia, SC 29208, United States;5. School of Management, Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China;1. School of Management, Harbin Institute of Technology, 92 West Dazhi Street, Harbin, China;2. Howard Feiertag Department of Hospitality and Tourism Management, Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061, USA;1. School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia;2. Centre for Data Science and Analytics, Taylor’s University, Subang Jaya, Malaysia;3. College of Computing and Informatics, Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates;4. Department of Management Sciences, COMSATS University Islamabad, Islamabad, Pakistan;5. Department of Information Sciences, University of Education, Lahore, Pakistan;6. Faculty of Computing and Informatics, University Malaysia Sabah, Labuan, Malaysia;7. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia;1. Northern Illinois University, NIU College of Business, 740 Garden Road DeKalb, IL 60115, USA;2. Louisiana State University, 2219 Business Education Complex South, 501 South Quad Drive, Baton Rouge, LA 70803, USA;3. University of Pavia, Via San Felice 5, 27100 Pavia, PV, Italy;4. Ming Chuan University, 5 De Ming Road, Gui Shan District, Taoyuan County 333, Taiwan |
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| Abstract: | This study considers the review reliability problem by identifying biased user-given ratings through rating prediction on the basis of the textual content. Deep learning approaches were introduced to investigate the textual review and validate the effect of rating prediction using a dataset collected from Yelp. The definition of “biased rating” was clarified and influenced the matching rules. The approach obtains high performance on a total of 1,000,000 reviews for prediction, with user-given ratings as the benchmark. Using the revealed biased ratings, unreliable reviews were detected by combining the results of several deep learning kernels. Findings shed light on understanding review quality by distinguishing biased ratings and unreliable reviews that may cause inconsistency and ambiguity to readers. Hence, theoretical and managerial areas for social media analytics are enriched on the basis of online review meta-data in hospitality and tourism. |
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| Keywords: | Online customer review Review reliability Review rating prediction Deep learning Information quality |
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