mlsanityData to 7 Oct 2026

Comparison of Topic Modelling Approaches in the Banking Context

arXiv 2402.03176 ↗First posted 5 Feb 2024cs.IR cs.AI cs.LGcited by 8

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Abstract (arXiv)

Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions. The use of traditional approaches such as Latent Dirichlet Allocation (LDA) for topic discovery has shown great performances, however, they are not consistent in their results as these approaches suffer from data sparseness and inability to model the word order in a document. Thus, this study presents the use of Kernel Principal Component Analysis (KernelPCA) and K-means Clustering in the BERTopic architecture. We have prepared a new dataset using tweets from customers of Nigerian banks and we use this to compare the topic modelling approaches. Our findings showed KernelPCA and K-means in the BERTopic architecture-produced coherent topics with a coherence score of 0.8463.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 65 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

10 of 65 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  5. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 378
  6. 2021Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detectioncited by 56
  7. 2022Topic Discovery via Latent Space Clustering of Pretrained Language Model Representationscited by 34
  8. 2016What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)cited by 14
  9. 2018Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaintscited by 7
  10. 2022Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddingscited by 5

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Cited by year

Citing papers per year: 2024: 4; 2025: 4.

8 papers.

  1. 2024The Use of a Large Language Model for Cyberbullying Detectioncited by 4
  2. 2024An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Marketcited by 4
  3. 2024A Systematic Review of Generative AI for Teaching and Learning Practicecited by 3
  4. 2025Recommending the right academic programs: An interest mining approach using BERTopiccited by 1
  5. 2025Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classificationcited by 1
  6. 2024An Iterative Approach to Topic Modellingcited by 0
  7. 2025Enhancing BERTopic with Intermediate Layer Representationscited by 0
  8. 2025LLM-Assisted Topic Reduction for BERTopic on Social Media Datacited by 0