  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.
