Comparison of Topic Modelling Approaches in the Banking Context

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.

Automated summary

Topic modelling is a crucial task in various applications, and traditional approaches like LDA have shown good performance but lack consistency due to data sparseness and inability to understand word order. This study introduces the use of KernelPCA and K-means clustering in the BERTopic architecture, which produced coherent topics with a high coherence score of 0.8463 when applied to a new dataset of tweets from Nigerian bank customers.