Identification of Topics from Scientific Papers through Topic Modeling

Topic modeling is a probabilistic model that identifies topics covered in text(s). In this paper, were loaded from two implementations of topic modeling, namely, Latent Semantic Indexing (LSI) and Dirichlet Allocation (LDA). This analysis was performed corpus 1000 academic papers written English, obtained PLOS ONE website, the areas Biology, Medicine, Physics Social Sciences. The objective to verify if four fields represented by modeling. (LDA) did not represent fields.