Table_1_Exploring Sources of Satisfaction and Dissatisfaction in Airbnb Accommodation Using Unsupervised and Supervised Topic Modeling.DOCX

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.

本研究旨在通过分析在线评论，探究影响爱彼迎（Airbnb）用户满意度与不满情绪的核心属性。本研究选取覆盖12座不同城市的27980套房源的59766条爱彼迎评论构建语料库，并采用潜在狄利克雷分配（Latent Dirichlet Allocation, LDA）与监督式潜在狄利克雷分配（supervised LDA, sLDA）两种方法开展分析。与以往基于潜在狄利克雷分配的爱彼迎相关研究不同，本研究将爱彼迎的好评与差评分开分析，研究结果揭示了爱彼迎住宿中满意度与不满情绪相关属性的异质性。尤为值得关注的是，本研究中识别出的“宾客冲突”主题，为未来共享经济住宿研究指明了全新方向，即探究高度共享环境下不同宾客间的互动行为。主题分布分析结果表明，针对不同类型的爱彼迎房源，用户对同类服务属性的重视程度存在显著差异。主题相关性分析则显示，“家一般的住宿体验”与“房东提供的协助”与爱彼迎用户的复住意愿显著相关。通过监督式潜在狄利克雷分配分析，本研究确定了对爱彼迎用户满意度与不满情绪预测能力最强的属性，这为爱彼迎从业者制定提升用户满意度的策略时确定优先级提供了极具价值的管理启示。从方法论层面而言，本研究通过展示如何运用创新方法将社交媒体数据转化为有关客户满意度的实用知识，做出了学术贡献，其研究结果也可为爱彼迎从业者提供宝贵的管理借鉴。