Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints

摘要

The Consumer Financial Protection Bureau (CFPB), created by congress in 2011, receives and processes consumer complaints pertaining to various financial services. Every complaint narrative provides insight into problems that consumers are experiencing. With increasing number of the CFPB complaint narratives, manual review of these documents by human experts is not feasible. This requires an intelligent system to analyze narratives automatically and provide insightful knowledge to the experts. In this paper, we propose an intelligent approach based on latent Dirichlet allocation (LDA) to analyze the CFPB consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time. The time trends will then be used to evaluate the effectiveness of the CFPB regulations and expectations on financial institutions in creating a consumer oriented culture. The technology-human partnership between the proposed approach and the CFPB experts could certainly improve consumer experience by providing more efficient and effective investigations of consumer complaint narratives. (C) 2019 Elsevier Ltd. All rights reserved.