Keywords
Abstract
Mazkur tadqiqotda Telegram kanallaridagi matnlarni mavzuga ko‘ra avtomatik guruhlash jarayonida ikki xil vektorlashtirish yondashuvi — TF-IDF va Sentence-BERT —ning samaradorligi solishtirildi. Dastlab kanal xabarlari tozalanib, standart shaklga keltirildi. TF-IDF statistik xususiyatlarga asoslangan yuqori o‘lchamli vektorlarni yaratdi, Sentence-BERT esa qisqa Telegram xabarlarining semantik mazmunini chuqur aks ettiruvchi kontekstual embeddinglar hosil qildi. Har ikki yondashuvda K-Means algoritmi qo‘llanib, natijalar siluet ko‘rsatkichi, Davies–Bouldin indeksi va qo‘lda semantik tahlil orqali baholandi.
Tadqiqot natijalariga ko‘ra, semantik jihatdan izchil va mavzuviy jihatdan bir xil klasterlar shakllantirishda Sentence-BERT TF-IDFga nisbatan ancha ustun ekanligi isbotlandi.
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