# Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints

arXiv: https://arxiv.org/abs/1807.07468 · HTML: https://mlsanity.com/paper/1807.07468/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [Comparison of Topic Modelling Approaches in the Banking Context](https://mlsanity.com/paper/2402.03176/) (2024)
- [HTMOT : Hierarchical Topic Modelling Over Time](https://mlsanity.com/paper/2112.03104/) (2021)
- [Reliability of Topic Modeling](https://mlsanity.com/paper/2410.23186/) (2024)
- [From Complaint Narratives to Monetary Relief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints](https://mlsanity.com/paper/2606.22664/) (2026)
- [n-stage Latent Dirichlet Allocation: A Novel Approach for LDA](https://mlsanity.com/paper/2110.08591/) (2021)
- [Text Mining-Based Patent Analysis for Automated Rule Checking in AEC](https://mlsanity.com/paper/2212.05891/) (2022)
- [Multi-generational labour markets: data-driven discovery of multi-perspective system parameters using machine learning](https://mlsanity.com/paper/2302.10146/) (2023)

## Abstract (arXiv)

A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (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 that treats consumers fairly and prioritizes consumer protection in their decision making processes. The proposed approach can be easily operationalized as a decision support system to automate detection of emerging topics in consumer complaints. Hence, the technology-human partnership between the proposed approach and the CFPB team could certainly improve consumer protections from unfair, deceptive or abusive practices in the financial markets by providing more efficient and effective investigations of consumer complaint narratives.