Using deep learning and visual analytics to explore hotel reviews and responses |
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| Institution: | 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;1. Fritz Knoebel School of Hospitality Management, Daniels College of Business, University of Denver, United States;2. Center of Economic Excellence in Tourism and Economic Development, School of Hotel, Restaurant and Tourism Management, University of South Carolina, United States;3. Huaqiao University, Quanzhou, Fujian Province, PR 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;3. School of Business, Sichuan University, 29 Wangjiang Road, Chengdu, China;1. Facultad de Ciencias Económicas y Empresariales. University of Seville (Spain), Av. de Ramón y Cajal, 1, 41018, Sevilla, Spain;2. E. S. Ingenieros, University of Seville (Spain), Avda. Camino de Los Descubrimientos s/n, 41092, Seville, Spain |
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| Abstract: | This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study. |
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| Keywords: | Deep learning Convolutional neural network Natural language processing Visual analytics Hospitality Tourism |
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