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Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining

机译：客户投诉在社交媒体推特上使用文本挖掘的社交媒体推特聚集

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摘要

Social media is considered one of the most effective platforms to communicate between companies and customers. Frequently, the customer of a product or service sends complaints via social media. Customers’ complaint data serve as a good suggestion for companies and organizations to improve their products and services. With the increasing number of customer complaints that have entered through social media accounts, government-owned drinking water companies need a more efficient way to extract information from complaint data. In this research, text mining is used to extract information about customer complaints against drinking water companies from social media Twitter. Latent Dirichlet Allocation (LDA) and self-organizing maps (SOM) approach is applied to model complaint topics and find out which are most frequently complained. The test results indicate grouping the data into five classes is the most appropriate model. Pipes leakage are the most frequently reported topics, 27.8% of total datasets.
机译：社交媒体被认为是公司与客户之间最有效的平台之一。通常，产品或服务的客户通过社交媒体发送投诉。客户的投诉数据是公司和组织提高产品和服务的良好建议。随着通过社交媒体账户进入的客户投诉的数量越来越多，政府所有的饮用水公司需要更有效的方法来提取投诉数据的信息。在这项研究中，文本挖掘用于提取有关客户投诉的信息，从社交媒体推特上提取饮用水公司。潜在的Dirichlet分配（LDA）和自组织地图（SOM）方法适用于模型投诉主题，并找出最常抱怨的内容。测试结果表明将数据分组为五个类是最合适的模型。管道泄漏是最常报告的主题，总数据集的27.8％。

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