Purpose: This 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/approach: This 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. Findings: Findings 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 implications: The 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/value: The 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.
|Journal||International Journal of Contemporary Hospitality Management|
|Number of pages||14|
|Publication status||Published - 8 Nov 2023|
Bibliographical noteFunding Information:
This study was supported by Research Fund of Sichuan University [grant number SKSYL2022-04]; National Natural Science Foundation of China [grant number 41871033]; The Sichuan Province Science and Technology Plan Project [grant number 2020YFS0062]; Teaching Reform Project of Sichuan Province [grant number JG2021-391]; Teaching Reform Project of Sichuan University [grant number SCU8115]; Regional History and Frontier Studies of Sichuan University.
- Critical reflection
- Deep learning
- Literature review
- Methodological review
- Text classification