Comparative Analysis of Deep Learning and Traditional Machine Learning Models for Turkish Text Classification

Abstract

In this study, using the word embedding method Doc2vec, the Turkish Text Classification 3600 (TTC-3600) dataset consisting of Turkish news texts was classified based on deep learning. Most commonly used classifiers were selected: Convolutional Neural Network (CNN), Gauss Naive Bayes (GNB), Random Forest (RF), Naïve Bayes (NB) and Support Vector Machine (SVM). While investigating the effect of text preprocessing steps on the success rate in the study, the results are compared with the previous studies with the TTC-3600 dataset. In the proposed model, a better accuracy rate was achieved with a result of 94.17% compared to the studies in the literature.