The work deals with the development of models and algorithms for intellectual analysis of data in the analysis and classification of textual data. With the help of the developed methods and models, it is used to solve the problems of classifying social network correspondence, determining its emotional relevance and evaluating it. A grouping structure of a cascaded neuro-fuzzy classifier was developed for the application of neural networks. A classification algorithm has been developed for the analysis of social networks (texts in Uzbek language), with the help of which the proposal of tools for columnarization and thesaurus implementation has been described.
| Mualliflar | Turakulov, Otabek, Mamaraufov, Odil |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2024-12-28 |
| Son | 4 |
| Betlar | 366-370 |
| Til | Rus |
ijtimoit tarmoq matnlari, neyro-noravshan tasniflagich, hissiyotlarni tahlil qilish, hissiylik bahosi, tezaurus, matnni tasniflash mexanizmi, LSTM neyron tarmog‘i., тексты социальных сетей, нейро-нечеткий классификатор, анализ настроений, оценка настроений, тезаурус, механизм классификации текстов, нейронная сеть LSTM
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