# Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddings

arXiv: https://arxiv.org/abs/2205.07259 · HTML: https://mlsanity.com/paper/2205.07259/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 15 May 2022.

## Builds on (most cited first, 3 of 3)

- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)](https://mlsanity.com/paper/1608.08176/) (2016)
- [Integrating Document Clustering and Topic Modeling](https://mlsanity.com/paper/1309.6874/) (2013)

## Built on by (most cited first, 5 of 5)

- [Comparison of Topic Modelling Approaches in the Banking Context](https://mlsanity.com/paper/2402.03176/) (2024)
- [Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian](https://mlsanity.com/paper/2402.03067/) (2024)
- [Mapping STI ecosystems via Open Data: overcoming the limitations of conflicting taxonomies. A case study for Climate Change Research in Denmark](https://mlsanity.com/paper/2209.08920/) (2022)
- [Mapping Climate Change Research via Open Repositories & AI: advantages and limitations for an evidence-based R&D policy-making](https://mlsanity.com/paper/2209.09246/) (2022)
- [Enhancing BERTopic with Intermediate Layer Representations](https://mlsanity.com/paper/2505.06696/) (2025)

## Abstract (arXiv)

Customers' reviews and comments are important for businesses to understand users' sentiment about the products and services. However, this data needs to be analyzed to assess the sentiment associated with topics/aspects to provide efficient customer assistance. LDA and LSA fail to capture the semantic relationship and are not specific to any domain. In this study, we evaluate BERTopic, a novel method that generates topics using sentence embeddings on Consumer Financial Protection Bureau (CFPB) data. Our work shows that BERTopic is flexible and yet provides meaningful and diverse topics compared to LDA and LSA. Furthermore, domain-specific pre-trained embeddings (FinBERT) yield even better topics. We evaluated the topics on coherence score (c_v) and UMass.