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 …

analysis applications banking comparison context cs.ai cs.ir cs.lg customer discovery discussions extraction industries lda modelling performances recommendation recommendation systems sentiment sentiment analysis service stat.co systems vital