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Comparison of topic modelling approaches in the banking context
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Ogunleye, Bayode, Maswera, Tonderai, Hirsch, Laurence, Gaudoin, Jotham and Brunsdon, Teresa (2023) Comparison of topic modelling approaches in the banking context. Applied Sciences, 13 (2). 797. doi:10.3390/app13020797 ISSN 2076-3417.
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Official URL: http://doi.org/10.3390/app13020797
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.
Item Type: | Journal Article | ||||||
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Subjects: | H Social Sciences > HF Commerce H Social Sciences > HG Finance Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Library of Congress Subject Headings (LCSH): | Banks and banking -- Technological innovations, Artificial intelligence -- Industrial applications, Marketing -- Mathematical models, Machine learning, Data mining, Kernel functions, Big data -- Economic aspects | ||||||
Journal or Publication Title: | Applied Sciences | ||||||
Publisher: | MDPI | ||||||
ISSN: | 2076-3417 | ||||||
Official Date: | 6 January 2023 | ||||||
Dates: |
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Volume: | 13 | ||||||
Number: | 2 | ||||||
Article Number: | 797 | ||||||
DOI: | 10.3390/app13020797 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 1 March 2023 | ||||||
Date of first compliant Open Access: | 1 March 2023 |
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