Author and genre identification of Turkish news texts using deep learning algorithms
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Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer India
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Nowadays, the increasing amount of data has brought the need to classify the data. Text classification is the process of categorizing similar text data. This paper aims to make a modeling study for author and genre identification, which is one of the important challenges of text classification, for Turkish news texts by using machine and deep learning algorithms. For this purpose, firstly, a total of 13 large-scale datasets having multi classes are built as new datasets. In the modeling stage, Multinomial Naive Bayes (MNB), Random Forest (RF), Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM) algorithms were applied to the datasets. Results showed that for dataset AI-TNKU-7, the CNN algorithm demonstrated the highest accuracy for author identification at 95.81%. In relation to genre identification, the LSTM algorithm for the dataset GI-TNKU-6 demonstrated the highest accuracy at 96.73%.
Açıklama
Anahtar Kelimeler
Author Identification, Genre Identification, Deep Learning, Text Classification, Turkish News Datasets, Machine Learning, Categorization
Kaynak
Sadhana-Academy Proceedings In Engineering Sciences
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
47
Sayı
4
