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

2022/05/15 by Vasudeva Raju Sangaraju, Bharath Kumar Bolla, Sangaraju, Vasudeva Raju +5 · 1 citation
Computer Science · Social Sciences · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computational and Text Analysis Methods #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information Theory (cs.IT) #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining

paper · pdf · doi:10.48550/arxiv.2205.07259

openalex publication_date 2022/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• Enhancing BERTopic with Intermediate Layer Representations

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