Abstract or Full-text in Topic Modeling? Konu Modellemede Özet mi Tam Metin mi?

Date

2022-01-01

Author

Tekin, Yasar
Coşar, Ahmet

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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.

Subject Keywords

abstract, full-text, LDA, parameter optimization, topic modeling

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138679793&origin=inward
https://hdl.handle.net/11511/101687

DOI

https://doi.org/10.1109/siu55565.2022.9864707

Conference Name

30th Signal Processing and Communications Applications Conference, SIU 2022

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

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Citation Formats

Y. Tekin and A. Coşar, “Abstract or Full-text in Topic Modeling? Konu Modellemede Özet mi Tam Metin mi?,” presented at the 30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 2022, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138679793&origin=inward.