Strategic decision-making in business is improved through business analytics, which can be descriptive, diagnostic, predictive or prescriptive. Business Analytics uses statistical methods on historic data to gain new perceptions for better policymaking. Topic modelling is a statistical, text-mining, unsupervised machine learning model, that can decipher themes from a corpus such as social media posts, annual reports, social media posts, news covers, related articles, trends in the domain, etc., In this research, Topic modelling is applied to Business Digital Economy Dataset which hosts around 2400 titles, abstracts, keywords from different authors related to various topics on digital economy. Topic modelling has various approaches of which, Latent Dirichlet Allocation (LDA) is most widely used. This paper explores the research articles related to emerging trends in business economy to extract the concealed semantic structures and generate word clouds. The research procedure comprises, introduction of dataset, data pre-processing, building and visualizing the model.