Author and genre identification of Turkish news texts using deep learning algorithms

Basit Öğe Kaydı
dc.authoridBEKTAS KOSESOY, Melike/0000-0002-1944-1928
dc.authorscopusid11539603200
dc.authorscopusid57896473800
dc.contributor.authorTüfekçi, Pınar
dc.contributor.authorBektaş, Melike
dc.date.accessioned2023-04-20T08:04:11Z
dc.date.available2023-04-20T08:04:11Z
dc.date.issued2022
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractNowadays, 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%.
dc.identifier.doi10.1007/s12046-022-01975-3
dc.identifier.issn0256-2499
dc.identifier.issn0973-7677
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85138352526
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s12046-022-01975-3
dc.identifier.urihttps://hdl.handle.net/20.500.11776/10987
dc.identifier.volume47
dc.identifier.wosWOS:000855450800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTüfekçi, Pınar
dc.language.isoen
dc.publisherSpringer India
dc.relation.ispartofSadhana-Academy Proceedings In Engineering Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAuthor Identification
dc.subjectGenre Identification
dc.subjectDeep Learning
dc.subjectText Classification
dc.subjectTurkish News Datasets
dc.subjectMachine Learning
dc.subjectCategorization
dc.titleAuthor and genre identification of Turkish news texts using deep learning algorithms
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
10987.pdf
Boyut:
950.17 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
Çorlu Mühendislik Fakültesi Koleksiyonu