Akademik Veri Yönetim Sistemi
JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 2024 (ESCI, TRDizin)
Text classification and relational analysis in Turkish play a critical role in understanding the language's complex structure and enhancing natural language processing (NLP) procedures. This study focuses on the classification of Turkish texts and the in-depth analysis of the relationships between them. The aim of the study is to develop an advanced classification model that effectively captures the rich morphological structure of Turkish and the intertextual relationships. Using a dataset obtained from the TRT-Haber website, graphbased deep learning techniques were employed to create a high-performance model. The BERT (BertTurk) model was used for semantic vector representations of texts, and adjacency matrices representing intertextual relationships were integrated. These data were then fed into a graph neural network (GNN) based classification model. The results demonstrate that the GNN model can classify texts with a remarkable accuracy rate of 97.93% and successfully resolve relational structures. These findings highlight the effectiveness and potential of graph-based approaches in text classification and relational analysis, contributing to the development of innovative methods for better understanding and processing Turkish texts.