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conference abstract · Signal Processing and Communications Applications Conference (SIU) · 2022

Abstract or Full-text in Topic Modeling?

Yaşar Tekın, Ahmet Coşar

Cited by 8Open access

Abstract

Topic modeling is a text mining technique used for automatic extraction of topics addressed in document collections. Although there are different topic models proposed by researchers, the most preferred one is Latent Dirichlet Allocation (LDA). Despite such widespread use, uncertainties about LDA have not been fully resolved yet. In this study, the effect of using abstracts or full-text articles on LDA model parameters is investigated. For this purpose, LDA parameters are optimized on abstracts and full-texts of articles published in two different scientific journals and the results obtained are compared with each other.

Cite this paper

APA 7

Tekın, Y., & Coşar, A. (2022). Abstract or Full-text in Topic Modeling? Signal Processing and Communications Applications Conference (SIU), 1–4. https://doi.org/10.1109/siu55565.2022.9864707

MLA 9

Tekın, Yaşar, and Ahmet Coşar. “Abstract or Full-text in Topic Modeling?” Signal Processing and Communications Applications Conference (SIU), 2022, pp. 1–4, https://doi.org/10.1109/siu55565.2022.9864707.

Chicago

Tekın, Yaşar, and Ahmet Coşar. 2022. “Abstract or Full-text in Topic Modeling?” Signal Processing and Communications Applications Conference (SIU): 1–4. https://doi.org/10.1109/siu55565.2022.9864707.

Harvard

Tekın, Y. and Coşar, A. (2022) ‘Abstract or Full-text in Topic Modeling?’, Signal Processing and Communications Applications Conference (SIU), pp. 1–4. Available at: https://doi.org/10.1109/siu55565.2022.9864707.

BibTeX
@article{tekn2022abstract,
  title = {{Abstract or Full-text in Topic Modeling?}},
  author = {Tekın, Yaşar and Coşar, Ahmet},
  journal = {Signal Processing and Communications Applications Conference (SIU)},
  pages = {1--4},
  year = {2022},
  doi = {10.1109/siu55565.2022.9864707}
}

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