We develop an NLP-based procedure for detecting systematic nonmeritorious consumer complaints, simply called systematic anomalies, among complaint narratives. While classification algorithms are used to detect pronounced anomalies, in the case of smaller and frequent systematic anomalies, the algorithms may falter due to a variety of reasons, including technical ones as well as natural limitations of human analysts. Therefore, as the next step after classification, we convert the complaint narratives into quantitative data, which are then analyzed using an algorithm for detecting systematic anomalies. We illustrate the entire procedure using complaint narratives from the Consumer Complaint Database of the Consumer Financial Protection Bureau.

翻译：我们提出了一种基于自然语言处理（NLP）的程序，用于在消费者投诉叙述中检测系统性的非正当投诉（简称为系统性异常）。尽管分类算法可用于检测显著异常，但对于较小且频繁出现的系统性异常，由于技术因素以及人类分析师的固有局限等多种原因，这些算法可能效果不佳。因此，在分类步骤之后，我们将投诉叙述转化为定量数据，随后利用一种检测系统性异常的算法对其进行分析。我们通过消费者金融保护局（CFPB）的消费者投诉数据库中的投诉叙述，完整演示了这一流程。