Digital marketers face challenges distilling vast review corpora into content themes. This LDA study on 30K Amazon reviews extracts interpretable topics having delivery = 0.12 prob, optimal at 10 topics with UMass score = 0.45. In this era of social media, content is the first thing that hooks a prospective customer, motivates them to engage with the content and eventually drives conversions. This will further lead to the improvement in the process of selling and buying of products based upon its content engagement which can be referred to as ‘ Content Marketing ’. Thus, analyzing these digital text corpora has become an important assignment. In this study, we explore text mining & probabilistic topic-model referred as Latent Dirichlet Allocation (LDA). The study undertaken involves users’ reviews on e-Commerce market visualization with topic-modelling for digital marketers. Visualization aims to deliberate documents’ topic modeling that would have some topic perspective solution on examining & creating a recommender database of associated articles. The application of this study proves to be a useful computation tool towards evaluation of different perspectives through social platforms and business researchers. This study reports integrated results in document-topic labels’ modeling analogous to dimensionality reduction, perplexity and validation. The digital marketers could utilize the topic modelling analysis towards increasing their business profits as it provides them with the precise topic labels from the large corpora of customer feedback.
Bangia et al. (Wed,) studied this question.