Purpose

Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews.

Design/methodology/approach

The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision).

Findings

The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses.

Originality/value

The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services.

中文翻译：

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在评论和重复访问之间：使用混合方法捕获重复访问者

目的

了解客户的重访行为在服务业领域尤为突出，网络社区的出现使客户能够表达他们之前的体验。因此，本研究的目的是调查客户在在线酒店预订平台上的评论，并从他们的评论中探索他们的后行为。

设计/方法/方法

作者采用了两种不同的方法并比较了预测客户发布行为的准确性：(1) 使用基于客户评论情感维度的几种机器学习分类器；(2) 进行由两个子部分组成的实验。在实验中，第一小节旨在让参与者预测写过评论的顾客是否会再次光顾酒店（称为预测），而第二小节则检查参与者在阅读其他顾客时是否想访问其中一家特定酒店'评论（称为决定）。

发现

机器学习方法的准确率（73.23%）高于实验方法（预测：58.96% 和决策：64.79%）。通过定性分析确定用户预测和决策的关键原因。

原创性/价值

研究结果表明，使用机器学习方法显示仅基于所采用的情感特征预测客户重复访问的准确性更高。通过整合客户决策过程和机器学习分类器的新颖方法，作者为酒店服务的研究人员和供应商提供了宝贵的见解。