(untitled) (American Journal of Engineering , Mechanics and Architecture (2993-2637), 2025)
This article provides an in-depth analysis of the capabilities of the BERT (Bidirectional Encoder Representations from Transformers) model, which holds a significant place in the field of Natural Language Processing (NLP), for performing deep, contextual, and semantic data analysis. The internal architecture and working mechanisms of the BERT model are explored, along with advanced approaches such as DistilBERT and Topic BERT-BiGRU, focusing on methodologies for accurately classifying both complex and short texts[5]. The paper also presents a comparative analysis with traditional models, clearly outlining the advantages of modern approaches. [1] Main Results: According to the research findings, approaches based on the BERT model demonstrate high accuracy in contextual and semantic text analysis. Specifically, the Topic BERT-BiGRU model stood out with an F1-score of 86.91% on short texts. DistilBERT outperformed the classic BERT model by 0.8–1.2% in accuracy on smaller datasets. Transformer-based classification systems, especially when combined with the Random Forest classifier, achieved over 90% accuracy. These approaches confirm the high efficiency of the BERT model in real-world applications.