Text Authorship Identification Based On Ensemble Learning and Genetic Algorithm Combination in Turkish Text

Year 2022, Volume: 25 Issue: 3, 1287 - 1297, 01.10.2022

Merve Güllü Hüseyin Polat

https://doi.org/10.2339/politeknik.992493

Cited By: 3

Abstract

The easiness of reaching information through the internet and social media and the expansiveness of opportunities for searching, copying, and spreading data have caused some problems in identifying an author for a specific text. A text carries the characteristic features of the person who wrote it, and these features can be used to identify its author. For this study, we are offering a method that is based on an approach using ensemble learning algorithm (ELA) and genetic algorithm (GA) for author identification in Tur-kish texts. The raw data set, which includes 40 authors and 3269 texts, was created from Turkish news websites and analyzed in pre-processing step. After, syntactic and structural analyses were done on the data and, in total, 6 different data sets were created. Each of the data sets was subjected to the feature selection process by using GA and ELA approach together. Each of the obtained data sets from the previous step was classified by using the ELA's bagging method which contains 5 different classifiers, namely, Naive Bayes, K-Nearest Neighbor, Artificial Neural Networks, Support Vector Machine, and Decision Tree. After applying the aforementioned processes to the raw data, the author identification approach reached 89% accuracy. The combination of ELA and GA has a strong potential to identify the author of a text.

Keywords

author identification, ensemble learning, genetic algorithm, feature selection

Türkçe Metinde Topluluk Öğrenme ve Genetik Algoritma Kombinasyonu Tabanlı Yazar Tahmini

Abstract

İnternet ve sosyal medya aracılığıyla bilgiye ulaşmanın kolaylaşması ve veri arama, kopyalama ve yayma olanaklarının geniş olması, belirli bir metin için yazar belirlemede bazı sorunlara neden olmuştur. Bir metin, onu yazan kişinin karakteristik özelliklerini taşır ve bu özellikler onun yazarını belirlemek için kullanılabilir. Bu çalışma için, Türkçe metinlerde yazar tespiti için topluluk öğrenme algo-ritması (TÖA) ve genetik algoritma (GA) kullanan bir yaklaşıma dayalı bir yöntem sunuyoruz. 40 yazar ve 3269 metinden oluşan ham veri seti Türkçe haber sitelerinden oluşturulmuş ve ön işleme aşamasında analiz edilmiştir. Daha sonra veriler üzerinde sözdi-zimsel ve yapısal analizler yapılmış ve toplamda 6 farklı veri seti oluşturulmuştur. Veri setlerinin her biri, GA ve TÖA yaklaşımı birlikte kullanılarak öznitelik seçim sürecine tabi tutulmuştur. Bir önceki adımdan elde edilen veri setlerinin her biri, TÖA'nın Naive Bayes, K-En Yakın Komşu, Yapay Sinir Ağları, Destek Vektör Makinesi ve Karar Ağacı olmak üzere 5 farklı sınıflandırıcı içeren torbalama yöntemi kullanılarak sınıflandırılmıştır. Ham verilere yukarıda bahsedilen işlemler uygulandıktan sonra yazar belirleme yaklaşımı %89 doğruluğa ulaşmıştır. TÖA ve GA kombinasyonu, bir metnin yazarını belirlemek için güçlü bir potansiyele sahiptir.

Keywords

yazar tespiti, topluluk öğrenme, genetik algoritma, özellik seçimi

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