Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints. (1st August 2019)
- Record Type:
- Journal Article
- Title:
- Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints. (1st August 2019)
- Main Title:
- Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints
- Authors:
- Bastani, Kaveh
Namavari, Hamed
Shaffer, Jeffrey - Abstract:
- Highlights: The Consumer Financial Protection Bureau takes consumer complaint narratives. A decision support system (DSS) for CFPB consumer complaint analysis is proposed. The mechanism is based on topic modeling to automatically reveal consumer issues. The extracted topics reveal interesting insights into the financial community. Success of federal consumer protection regulations are examined via the proposed DSS. Abstract: 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 byHighlights: The Consumer Financial Protection Bureau takes consumer complaint narratives. A decision support system (DSS) for CFPB consumer complaint analysis is proposed. The mechanism is based on topic modeling to automatically reveal consumer issues. The extracted topics reveal interesting insights into the financial community. Success of federal consumer protection regulations are examined via the proposed DSS. Abstract: 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. … (more)
- Is Part Of:
- Expert systems with applications. Volume 127(2019)
- Journal:
- Expert systems with applications
- Issue:
- Volume 127(2019)
- Issue Display:
- Volume 127, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 127
- Issue:
- 2019
- Issue Sort Value:
- 2019-0127-2019-0000
- Page Start:
- 256
- Page End:
- 271
- Publication Date:
- 2019-08-01
- Subjects:
- Analytics -- Latent Dirichlet allocation -- Topic modeling -- CFPB -- Decision support system -- Consumer complaint narratives
Expert systems (Computer science) -- Periodicals
Systèmes experts (Informatique) -- Périodiques
Electronic journals
006.33 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09574174 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.eswa.2019.03.001 ↗
- Languages:
- English
- ISSNs:
- 0957-4174
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3842.004220
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9736.xml