The purpose of this study is to guide pricing policies of Airbnb accommodation rentals to reduce inefficient pricing strategies through a novel application of topic modelling and a fuzzy clustering. In particular, the method proposes the application of Structural Topic Modelling, which explains a set of observations from latent topics. The associations between topics by Fuzzy C-Means Clustering are analysed to obtain new, more compact representations of topics (i.e., metatopics). This research identifies 15-metatopics related to Airbnb accommodations based on location and connectivity, enjoyment of domestic and everyday services, and the possibility of more authentic local experiences, among others. The influence of key metatopics on the price of Airbnb accommodations is determined by applying Extreme Gradient Boosting (an efficient and scalable implementation of gradient boosting framework) and Shapley Additive Explanations values. To sum up, our research provides an explicit contribution of user-generated content to promote the development of mutually beneficial relationships between guests and hosts, and detects future lines of research and practical and conceptual implications of the findings.

中文翻译：

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预测Airbnb住宿价格的模糊变位论

这项研究的目的是通过主题建模和模糊聚类的新颖应用来指导Airbnb住宿租金的定价政策，以降低低效的定价策略。尤其是，该方法提出了“结构性主题建模”的应用，该结构性建模解释了来自潜在主题的一组观察结果。通过模糊C均值聚类分析主题之间的关联，以获得新的，更紧凑的主题表示（即，元主题）。这项研究基于位置和连接性，享受家庭和日常服务以及更真实的当地体验的可能性等来确定与Airbnb住宿相关的15个主题。关键元主题对Airbnb住宿价格的影响是通过应用“极端梯度增强”（梯度增强框架的一种高效且可扩展的实现）和“ Shapley Additive解释”值来确定的。综上所述，我们的研究为用户生成的内容提供了明确的贡献，以促进来宾与主人之间互利关系的发展，并检测出未来的研究方向以及研究结果的实际和概念含义。