Identification of Topics from Scientific Papers through Topic Modeling
Denis Luiz Marcello Owa
Abstract
Topic modeling is a probabilistic model that identifies topics covered in text(s). In this paper, topics were loaded from two implementations of topic modeling, namely, Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). This analysis was performed in a corpus of 1000 academic papers written in English, obtained from PLOS ONE website, in the areas of Biology, Medicine, Physics and Social Sciences. The objective is to verify if the four academic fields were represented in the four topics obtained by topic modeling. The four topics obtained from Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) did not represent the four academic fields.
Topics & Concepts
Latent Dirichlet allocationTopic modelProbabilistic latent semantic analysisIdentification (biology)Computer scienceSearch engine indexingProbabilistic logicLatent semantic analysisInformation retrievalDirichlet distributionNatural language processingData scienceArtificial intelligenceMathematicsMathematical analysisBoundary value problemBotanyBiologyAdvanced Text Analysis TechniquesTopic ModelingComputational and Text Analysis Methods