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Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., et al. (2019) Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey. Multimedia Tools and Applications, 78, 15169-15211.
https://doi.org/10.1007/s11042-018-6894-4

has been cited by the following article:

• TITLE: Identification of Topics from Scientific Papers through Topic Modeling

  AUTHORS: Denis Luiz Marcello Owa

  KEYWORDS: Topic Modeling, Corpus Linguistics, Gensim, LSI, LDA

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.11 No.4, April 29, 2021

  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.