COMPARATIVE ANALYSIS OF NEURAL MODELS FOR TEXT CLASSIFICATION PROBLEMS
The paper investigates the phenomenon of fake messages and approaches to their detection. A comparative analysis of the effectiveness of using different neural network models for the tasks of searching and classifying text fragments containing false messages is conducted. The influence of model’s dimension on the learning speed, detection accuracy, and ability to adapt to unknown data is investigated. Keywords: artificial intelligence, computational linguistics, neural network.
Authors
- Volodymyr Taranukha (ORCID: https://orcid.org/0000-0002-9888-4144)
- А. В. Анисимов (ORCID: https://orcid.org/0000-0002-1467-2006)
- Emil Nasirov (ORCID: https://orcid.org/0009-0006-9016-2602)
- Олександр Марченко (ORCID: https://orcid.org/0000-0002-5408-5279)
Publication Details
- Journal
- Kibernetyka ta Systemnyi Analiz
- Published
- 2025-01-01
- DOI
- https://doi.org/10.34229/kca2522-9664.25.3.1
- Primary Topic
- Statistical and Computational Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00