基于会话数据的电子商务客户体验核心研究
A Core of E-Commerce Customer Experience based on Conversational Data using Network Text Methodology
摘要：
电子商务提供了一种在卖家和客户之间交换货物的高效和有效的方式。电子商务已经成为一种流行的做生意的方法，因为它简单透明地提供商业活动，包括客户的声音和对自己体验的意见。这些经验可以是一个很大的好处，全面了解客户体验，无论是卖方和未来的客户。本文适用于印度尼西亚的电子商务和客户。许多印尼客户表达了开放Twitter和Facebook等社交网络服务的声音，其中很大一部分数据是以对话数据的形式存在的。通过开放的社交网络服务了解客户行为，我们可以了解印度尼西亚的电子商务服务水平。因此，这与政府改善印尼数字经济生态系统的努力有关。需要一种在大规模互联网非结构化文本数据中发现核心主题的方法，该方法应该是快速的，但足够准确。处理大规模数据不是一件简单的工作，它往往需要具有特殊技能的人和复杂的软硬件计算机系统。提出了一种基于频繁词及其词关联的快速文本挖掘方法，形成网络文本挖掘方法。该方法是从社会网络分析中改编而来的，通过模型来描述单词之间的关系，而不是演员之间的关系。
英文摘要：
E-commerce provides an efficient and effective way to exchange goods between sellers and customers. E-commerce has been a popular method for doing business, because of its simplicity of having commerce activity transparently available, including customer voice and opinion about their own experience. Those experiences can be a great benefit to understand customer experience comprehensively, both for sellers and future customers. This paper applies to e-commerces and customers in Indonesia. Many Indonesian customers expressed their voice to open social network services such as Twitter and Facebook, where a large proportion of data is in the form of conversational data. By understanding customer behavior through open social network service, we can have descriptions about the e-commerce services level in Indonesia. Thus, it is related to the government's effort to improve the Indonesian digital economy ecosystem. A method for finding core topics in large-scale internet unstructured text data is needed, where the method should be fast but sufficiently accurate. Processing large-scale data is not a straightforward job, it often needs special skills of people and complex software and hardware computer system. We propose a fast methodology of text mining methods based on frequently appeared words and their word association to form network text methodology. This method is adapted from Social Network Analysis by the model relationships between words instead of actors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
