Poet Classification Using ANN and DNN

Since statistical analysis of poetry is a challenging task in Natural Language Processing (NLP), making inferences about the poets also becomes a very challenging task. In this study, a dataset of Turkish poems which is obtained for 5 different poets is used to compare classification performance of the Artificial Neural Network (ANN) and Deep Neural Network (DNN) architectures. While Multilayer Perceptron (MLP) is selected for ANN architecture, Convolutional Neural Network (CNN) is selected as DNN architecture. Two main feature representation approaches are used for the experiments- Term Frequency-Inverse Document Frequency (TF-IDF) is used for ANN and word embedding is used for DNN. As a result of the experiments it has been seen that MLP has the highest performance in terms of accuracy, precision, recall and F-score.