Comparison of Topic Modelling Approaches in the Banking Context

• URL: http://arxiv.org/abs/2402.03176v1
• Date: Mon, 5 Feb 2024 16:43:53 GMT
• Title: Comparison of Topic Modelling Approaches in the Banking Context
• Authors: Bayode Ogunleye, Tonderai Maswera, Laurence Hirsch, Jotham Gaudoin, and Teresa Brunsdon
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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