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A text analytics approach for online retailing service improvement: Evidence from Twitter

机译：在线零售服务改进的文本分析方法：来自推特的证据

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摘要

The purpose of this study is to identify the customers' primary topics of concern regarding online retail brands that are shared among Twitter users. This study collects tweets associated with five leading UK online retailers covering the period from Black Friday to Christmas and New Year's sales. We use a combination of text analytical approaches including topic modelling, sentiment analysis, and network analysis to analyse the tweets. Through the analysis, we identify that delivery, product and customer service are among the most-discussed topics on Twitter. We also highlight the areas that receive the most negative customer sentiments such as delivery and customer service. Interestingly, we also identify emerging topics such as online engagement and in-store experience that are not captured by the existing literature on online retailing. Through a network analysis, we underscore the relationships among those important topics. This study derives insights on how well the leading online retail brands are performing and how their products and services are perceived by their customers. These insights can help businesses understand customers better and enable them to convert the information into meaningful knowledge to improve their business performance. The study offers a novel approach of transforming social media data into useful knowledge about online retailing. The incorporation of three analytical approaches offers insights for researchers to understand the hidden content behind the large collections of unstructured bodies of text, and this information can be used to improve online retailing services and reach out to customers.
机译：本研究的目的是识别客户对在推特用户共享的在线零售品牌的关注的主要主题。本研究收集了与五个领先的英国在线零售商相关的推文，涵盖了从黑色星期五到圣诞节和新年的销售时期的。我们使用文本分析方法的组合，包括主题建模，情感分析和网络分析来分析推文。通过分析，我们确定交付，产品和客户服务是Twitter最讨论的主题之一。我们还突出了接受最负面客户情绪的领域，如交付和客户服务。有趣的是，我们还确定了在线零售业上现有文献未被现有文献捕获的在线参与和店内经验之类的新出现的主题。通过网络分析，我们强调了这些重要主题之间的关系。这项研究始终介绍了领先的在线零售品牌表现的表现以及他们的产品和服务如何被客户所感受到的。这些见解可以帮助企业更好地了解客户，并使他们能够将信息转换为有意义的知识，以提高其业务绩效。该研究提供了一种将社交媒体数据转化为关于在线零售的有用知识的新方法。纳入三种分析方法为研究人员提供了解，了解大量非结构化案文中的隐藏内容，这些信息可用于改善在线零售服务并向客户联系。

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