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Automating tourism online reviews: a neural network based aspect-oriented sentiment classification

Nao Li (School of International Economics and Management, Beijing Technology and Business University, Beijing, China and State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China)
Xiaoyu Yang (Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China)
IpKin Anthony Wong (School of Tourism Management, Sun Yat-Sen University, Zhuhai, China)
Rob Law (Asia-Pacific Academy of Economics and Management, Department of Integrated Resort and Tourism Management, Faculty of Business Administration, University of Macau, Macau, China)
Jing Yang Xu (School of Computer Science and Technology, Beijing Technology and Business University, Beijing, China)
Binru Zhang (School of Finance and Economics, Yangtze Normal University, Chongqing, China)

Journal of Hospitality and Tourism Technology

ISSN: 1757-9880

Article publication date: 24 November 2022

Issue publication date: 11 January 2023




This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a neural network model.


This study constructs an aspect-oriented sentiment classification model using an integrated four-layer neural network: the bidirectional encoder representation from transformers (BERT) word vector model, long short-term memory, interactive attention-over-attention (IAOA) mechanism and a linear output layer. The model was trained, tested and validated on an open training data set and 92,905 reviews extrapolated from restaurants in Tokyo.


The model achieves significantly better performance compared with other neural networks. The findings provide empirical evidence to validate the suitability of this new approach in the tourism-hospitality domain.

Research limitations/implications

More sentiments should be identified to measure more fine-grained tourism-hospitality experience, and new aspects are recommended that can be automatically added into the aspect set to provide dynamic support for new dining experiences.


This study provides an update to the literature with respect to how a neural network could improve the performance of aspect-oriented sentiment classification for tourism-hospitality online reviews.




本研究使用集成的四层神经网络构建面向方面的情感分类模型:BERT 词向量模型、LSTM、IAOA 机制和线性输出层。该模型在一个开放的训练数据集和从东京餐厅推断的 92,905 条评论上进行了训练、测试和验证。


与其他神经网络相比, 该模型实现了显着更好的性能。研究结果提供了经验证据, 以验证这种新方法在旅游酒店领域的适用性。




应该识别更多的情感从而来更加细化衡量旅游酒店体验, 并推荐新的方面/维度可以被自动添加到方面集中, 为新的用餐体验提供动态支持。



Funding: This research was funded by a grant from State Key Laboratory of Resources and Environmental Information System, supported by the Humanities and Social Science Fund of Ministry of Education of China, grant number 20YJC630202 and the National Social Science Foundation of China, grant number 21XJY002.


Li, N., Yang, X., Wong, I.A., Law, R., Xu, J.Y. and Zhang, B. (2023), "Automating tourism online reviews: a neural network based aspect-oriented sentiment classification", Journal of Hospitality and Tourism Technology, Vol. 14 No. 1, pp. 1-20.



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