Detail publikace

Latent Dirichlet Allocation (LDA) Approximation Analysis of Financial-Related Text Messages

JANKOVÁ, Z.

Originální název

Latent Dirichlet Allocation (LDA) Approximation Analysis of Financial-Related Text Messages

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Topic modeling is one of the most widely used NLP techniques for discovering hidden patterns and latent relationships in text documents. Latent Dirichlet Allocation (LDA) is one of the most popular methods in this field. There are different approaches to tuning the parameters of LDA models such as Gibbs sampling, variational method, or expected propagation. This paper aims to compare individual LDA parameter tuning approaches with respect to their performance and accuracy on textual data from the financial domain. From our results, it can be concluded that for text datasets obtained from financial social platforms, stochastic solvers are the most suitable and especially less time consuming than approximation methods.

Klíčová slova

Financial Messages; Latent Dirichlet Allocation; LDA; NLP; Text analysis; Topic Modeling

Autoři

JANKOVÁ, Z.

Vydáno

1. 4. 2023

Nakladatel

Bucharest University of Economic Studies

Místo

Bucharest, Romania

ISSN

0424-267X

Periodikum

Economic Computation and Economic Cybernetics Studies and Research

Ročník

57

Číslo

1

Stát

Rumunsko

Strany od

267

Strany do

282

Strany počet

16

URL

https://ecocyb.ase.ro/nr2023_1/2023_1_17_ZuzanaJANKOVA_online.pdf

BibTex

@article{BUT183297,
  author="Zuzana {Janková}",
  title="Latent Dirichlet Allocation (LDA) Approximation Analysis of Financial-Related Text Messages",
  journal="Economic Computation and Economic Cybernetics Studies and Research",
  year="2023",
  volume="57",
  number="1",
  pages="267--282",
  doi="10.24818/18423264/57.1.23.17",
  issn="0424-267X",
  url="https://ecocyb.ase.ro/nr2023_1/2023_1_17_ZuzanaJANKOVA_online.pdf"
}