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Leveraging social media to gain insights into service delivery: a study on Airbnb

机译：利用社交媒体深入了解服务交付：Airbnb上的一项研究

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摘要

Consumers increasingly rely on reviews and social media posts provided by others to get information about a service. Especially in the Sharing Economy, the quality of service delivery varies widely; no common quality standard can be expected. Because of the rapidly increasing number of reviews and tweets regarding a particular service, the available information becomes unmanageable for a single individual. However, this data contains valuable insights for platform operators to improve the service and educate individual providers. Therefore, an automated tool to summarize this flood of information is needed. Various approaches to aggregating and analyzing unstructured texts like reviews and tweets have already been proposed. In this research, we present a software toolkit that supports the sentiment analysis workflow informed by the current state-of-the-art. Our holistic toolkit embraces the entire process, from data collection and filtering to automated analysis to an interactive visualization of the results to guide researchers and practitioners in interpreting the results. We give an example of how the tool works by identifying positive and negative sentiments from reviews and tweets regarding Airbnb and delivering insights into the features of service delivery its users most value and most dislike. In doing so, we lay the foundation for learning why people participate in the Sharing Economy and for showing how to use the data. Beyond its application on the Sharing Economy, the proposed toolkit is a step toward providing the research community with an instrument for a holistic sentiment analysis of individual domains of interest.
机译：消费者越来越依赖他人提供的评论和社交媒体帖子来获取有关服务的信息。特别是在共享经济中，服务提供的质量差异很大。没有共同的质量标准。由于有关特定服务的评论和推文数量迅速增加，因此单个人无法管理可用信息。但是，此数据包含有价值的见解，可供平台运营商改善服务并教育各个提供商。因此，需要一个自动工具来汇总这种大量信息。已经提出了各种汇总和分析非结构化文本（如评论和推文）的方法。在这项研究中，我们介绍了一个软件工具包，该工具包支持当前最新技术所提供的情感分析工作流。我们的整体工具包涵盖了整个过程，从数据收集和过滤到自动化分析到结果的交互式可视化，可指导研究人员和从业人员解释结果。我们通过从有关Airbnb的评论和推文中识别正面和负面情绪，并深入了解其用户最看重和最不喜欢的服务交付功能，来举例说明该工具的工作原理。通过这样做，我们为了解人们为什么参与共享经济以及展示如何使用数据奠定了基础。除了在共享经济中的应用外，拟议的工具包还迈出了向研究界提供用于对单个感兴趣领域进行整体情感分析的工具的一步。

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