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This study aims to examine the impact of the outsourcing of hotel departments on service quality measured through online customer reviews.

Three models were developed, considering three important online tourism reputation websites, to establish the relationship between the outsourcing of hotel activities and service quality.

The results show that in the three databases, hotel outsourcing has a negative influence on online reputation. A higher level of outsourcing reduces service quality, the percentage of recommendations and the value perceived by customers who carry out online reviews of these hotels. In addition, different models were established for each type of department.

To the best of the authors’ knowledge, this paper presents the first empirical study to analyse the relationship between the impact of process outsourcing and customers’ online reviews. It is also the first empirical research to consider the relationship between outsourcing and ratings by hotel end-customers as a performance measure.

通过分析在线顾客评论, 本研究旨在探索外包酒店部门活动对顾客服务质量的认知影响。

为探知外包酒店活动和服务质量之间的关系, 本研究借用了三个重要的旅游在线评论网站建立了三个模型。

研究结果表明再三项数据库中, 酒店外包对酒店的在线名声有负面影响。高程度的外包降低顾客对服务质量的评价, 推介率, 以及顾客对酒店的感知价值。此外, 本研究为酒店各项部门都建立了对应的不同模型。

本论文作为对酒店外包活动对顾客在线评论影响的首次实证研究。本研究也是首次探讨外包和顾客评分作为一项绩效指标的研究。

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.

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.

本文旨在从方面级对在线旅游-酒店评论的情感进行分类。提出了一种基于神经网络模型的面向方面的情感分类新方法。

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。

该研究提供了有关神经网络如何提高旅游酒店在线评论的面向方面的情感分类性能的新文献。

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。

This study aims to explore the success factors of tourism performing arts (TPA) programs by analyzing a large data set of online reviews.

A total of 195,230 reviews from Ctrip.com were collected and preprocessed. A deep learning method was leveraged to estimate the similarity between words. Then, regression analysis was conducted to determine success factors.

This study extracted four positive and two negative factors affecting tourist satisfaction with tourism performance arts. The results demonstrate that the tourists paid the most attention to the traditional Chinese cultural aspects, audiovisual effects and the actors’ performing enthusiasm.

Despite this study’s large data set, the focused was only on Chinese reviews. It would be useful and interesting to compare the success factors of tourism performance arts programs offered in different countries.

The study findings can contribute to the development of TPA programs to attract tourists to travel destinations.

This study demonstrates that analyzing online reviews of TPA through text mining technology is an effective method of understanding tourist satisfaction.

在线点评分析探析旅游演艺成功因素

本研究旨在通过分析大型在线评论数据集来探索旅游表演艺术项目的成功因素。

共收集和预处理来自携程网的 195,230 条评论。利用深度学习方法来估计单词之间的相似性。然后, 进行回归分析以确定成功因素。

本研究提取了影响游客对旅游表演艺术满意度的四个积极因素和两个消极因素。结果表明, 游客最关注中国传统文化方面、视听效果和演员的表演热情。

尽管我们的数据集很大, 但它只关注中文评论。比较不同国家提供的旅游表演艺术项目的成功因素将是有用和有趣的未来研究方向。

研究结果有助于发展旅游表演艺术项目, 以吸引游客前往旅游目的地。

研究表明, 通过文本挖掘技术分析旅游表演艺术的在线评论是了解游客满意度的有效方法。