Listening to the voice of the guest: A framework to improve decision-making processes with text data |
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| Affiliation: | 1. School of Business and Management, Federal University of Uberlandia, Brazil;2. School of Business Administration of São Paulo, Fundação Getúlio Vargas, Brazil;3. Performance Augmentation Lab, Oxford Brookes University, Oxford, UK;4. Faculty of Economic Sciences, Federal University of Rio Grande do Sul, Brazil;1. Conrad N. Hilton College of Hotel & Restaurant Management, University of Houston, 4450 University Dr. #227, Houston, TX, 77204, United States;2. Department of Hotel Management, Cheju Halla University, 28 Halladae-gil, Heungeop-myeon, Wonju-si, Gangwon-do, South Korea;3. Chaplin School of Hospitality & Tourism Management, Florida International University, Biscayne Bay Campus, 3000 Northeast 151 Street, North Miami, LF, 33181, United States;1. Dedman College of Hospitality Management, Florida State University, Tallahassee, FL, 32306-2541, United States;2. Conrad N. Hilton College of Hotel and Restaurant Management, University of Houston, Houston, TX, United States;3. School of Hotel, Restaurant and Tourism Management, University of South Carolina, Columbia, SC, 29208, United States;1. School of Hospitality Leadership, College of Business, East Carolina University, Rivers West 311, Greenville, NC 27858-4353, USA;2. Rosen College of Hospitality Management, University of Central Florida, 9907 Universal Blvd., Orlando, FL 32819, USA;1. College of Management, Shenzhen University, Shenzhen, China;2. School of Management, Xiamen University, Xiamen, China;3. Organizational Behavior and Human Resource Management Department, China Europe International Business School (CEIBS), Shanghai, China;4. Faculty of Business and Economics, The University of Hong Kong, Hong Kong, China;5. School of Management, CISME, Zhijiang College, Zhejiang University of Technology, Hangzhou, China |
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| Abstract: | The amount of texts available on the web is growing continuously and making sense of this unstructured data efficiently and effectively, therefore, poses a demanding challenge for organizations. Although computer science community has developed many techniques, there is ample room for improvement on organizational utilization of such text data, especially when referring to decision-making support. In this article, we propose and validate a framework towards an effective use of text data inside hotel industry, bringing tourism sector to this discussion. We combined three text mining techniques for text classification, sentiment analysis and topic modeling in a novelty way to allows managers to analyze guests’ comments and compares competitors in hospitality industry based on SERVQUAL. Our objective is to present an automatized process involving text data collection and analysis, improving decision-making process. |
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| Keywords: | Online reviews Text mining NLP Topic modeling Sentiment analysis |
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