This paper investigates the effectiveness of transformer-based models (mBERT, BERTbek, XLM-R, ALBERT, ELECTRA) in analyzing Telegram social media text data. The study represents text data as contextual embeddings and applies self-attention mechanisms to capture semantic relationships. The models’ tasks, advantages, and performance in real-world Telegram messages are analyzed. The results demonstrate that transformer models achieve high accuracy in detecting sentiment, threats, and harmful content. The paper also highlights the potential of hybrid approaches combining multiple models to improve overall performance.
| Mualliflar | Qo'yliyeva, Feruzaxon, Бабомурадов, Озод |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-05-17 |
| Son | 2 |
| Betlar | 176-181 |
| Til | Rus |
transformer models, social media, text analysis, BERT, mBERT, XLM-R, ELECTRA, harmful content, artificial intelligence, NLP, transformer modellar, ijtimoiy tarmoqlar, matn tahlili, BERT, mBERT, XLM-R, ELECTRA, xavfli kontent, sun’iy intellekt, NLP
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