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Applied Sciences
Volume 13
Issue 2
10.3390/app13020797
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Article

Comparison of Topic Modelling Approaches in the Banking Context

by
Bayode Ogunleye
1,*,
Tonderai Maswera
1,
Laurence Hirsch
1,
Jotham Gaudoin
1 and
Teresa Brunsdon
2
1
Department of Computing, Sheffield Hallam University, Sheffield S1 2NU, UK
2
Department of Statistics, University of Warwick, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(2), 797; https://doi.org/10.3390/app13020797
Submission received: 25 November 2022 / Revised: 30 December 2022 / Accepted: 3 January 2023 / Published: 6 January 2023
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Abstract

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.
Keywords: kernel pca; k-means clustering; topic extraction; topic model; aspect extraction; natural language processing; banking industry; Nigeria Pidgin English kernel pca; k-means clustering; topic extraction; topic model; aspect extraction; natural language processing; banking industry; Nigeria Pidgin English

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MDPI and ACS Style

Ogunleye, B.; Maswera, T.; Hirsch, L.; Gaudoin, J.; Brunsdon, T. Comparison of Topic Modelling Approaches in the Banking Context. Appl. Sci. 2023, 13, 797. https://doi.org/10.3390/app13020797

AMA Style

Ogunleye B, Maswera T, Hirsch L, Gaudoin J, Brunsdon T. Comparison of Topic Modelling Approaches in the Banking Context. Applied Sciences. 2023; 13(2):797. https://doi.org/10.3390/app13020797

Chicago/Turabian Style

Ogunleye, Bayode, Tonderai Maswera, Laurence Hirsch, Jotham Gaudoin, and Teresa Brunsdon. 2023. "Comparison of Topic Modelling Approaches in the Banking Context" Applied Sciences 13, no. 2: 797. https://doi.org/10.3390/app13020797

APA Style

Ogunleye, B., Maswera, T., Hirsch, L., Gaudoin, J., & Brunsdon, T. (2023). Comparison of Topic Modelling Approaches in the Banking Context. Applied Sciences, 13(2), 797. https://doi.org/10.3390/app13020797

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