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

机译：一种用于在线零售服务改善的文本分析方法：Twitter的证据

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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：这项研究的目的是确定与Twitter用户之间共享的在线零售品牌有关的客户主要关注主题。这项研究收集了与五家英国领先的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期间。我们使用文本分析方法（包括主题建模，情感分析和网络分析）的组合来分析推文。通过分析，我们确定交付，产品和客户服务是Twitter上讨论最多的主题。我们还将重点介绍那些受到负面客户情绪最多的领域，例如交付和客户服务。有趣的是，我们还确定了新兴的话题，例如在线参与和店内体验，这些都没有被有关在线零售的现有文献所涵盖。通过网络分析，我们强调了这些重要主题之间的关系。这项研究得出了关于领先的在线零售品牌表现如何以及客户如何看待其产品和服务的见解。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化为有意义的知识，从而改善他们的业务绩效。该研究提供了一种将社交媒体数据转换为有关在线零售的有用知识的新颖方法。三种分析方法的结合为研究人员提供了见识，使他们能够了解大量非结构化文本集背后的隐藏内容，并且该信息可用于改善在线零售服务并覆盖客户。

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