Analysis of Uzbek Social Media Texts Based on Transformer Models

Qo'yliyeva, Feruzaxon, Бабомурадов, Озод

Al-Farg'oniy avlodlari · 2026-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarQo'yliyeva, Feruzaxon, Бабомурадов, Озод
JurnalAl-Farg'oniy avlodlari
Nashr sanasi2026-05-17
Son2
Betlar176-181
TilRus

Kalit so‘zlar

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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