Text classification in tourism and hospitality - a deep learning perspective
文献类型：期刊论文
| 作者 | Liu, Jun; Hu, Sike; Mehraliyev, Fuad3; Liu, Haolong2 |
| 刊名 | INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT
 |
| 出版日期 | 2023-04-01 |
| 卷号 | N/A |
| 关键词 | Literature review Methodological review Text classification Deep learning Viewpoint Critical reflection |
| ISSN号 | 1757-1049 |
| DOI | 10.1108/IJCHM-07-2022-0913 |
| 文献子类 | Review; Early Access |
| 英文摘要 | PurposeThis study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research. Design/methodology/approachThis study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022. FindingsFindings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites. Practical implicationsThe applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes. Originality/valueThe paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set. |
| WOS关键词 | RATINGS |
| WOS研究方向 | Social Sciences - Other Topics ; Business & Economics |
| WOS记录号 | WOS:000952337500001 |
| 出版者 | EMERALD GROUP PUBLISHING LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190234]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Inst Geog Sci & Nat Resources Res CAS, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China 2.Roskilde Univ, Dept Social Sci & Business, Roskilde, Denmark 3.Sichuan Univ, Tourism Sch, Chengdu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jun,Hu, Sike,Mehraliyev, Fuad,et al. Text classification in tourism and hospitality - a deep learning perspective[J]. INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT,2023,N/A. |
| APA | Liu, Jun,Hu, Sike,Mehraliyev, Fuad,&Liu, Haolong.(2023).Text classification in tourism and hospitality - a deep learning perspective.INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT,N/A. |
| MLA | Liu, Jun,et al."Text classification in tourism and hospitality - a deep learning perspective".INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT N/A(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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