This article studies the effectiveness of modern deep learning models for the task of deep contextual analysis of text data in social networks. As part of the study, the ability of RNN, LSTM and DistilBERT models based on the transformer architecture to classify sentiment and topic (religious, social, political) based on a dataset compiled from user comments was compared. The dataset was first cleaned and normalized, and then the models were trained in a dual-task (sentiment and topic) mode.
| Mualliflar | Комила, Обидова |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-03-26 |
| Son | 1 |
| Betlar | 295-303 |
| Til | Rus |
Deep learning, , sentiment analysis, topic detection, social networks, RNN, LSTM, DistilBERT, transformer models, contextual analysis, text classification., глубокое обучение, анализ настроений, определение тем, социальные сети, RNN, LSTM, DistilBERT, модели трансформеров, контекстный анализ, Chuqur o‘qitish, sentiment tahlili, mavzu aniqlash, ijtimoiy tarmoqlar, RNN, LSTM, DistilBERT, transformer modellari, , kriptotahlil usullari, matn tasnifi.
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