Stilistik Özellikler Kullanılarak Yazar Tanıma İşinde Yapay Sinir Ağlarının Başarımının Değerlendirilmesi: Türkçe Köşe Yazıları
Yavanoğlu Milletsever, Özlem
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One of the main opportunities that the internet provides today is the rapidity of media resources, anonymity and accessibility from anywhere. Since individuals do not have to use their real identity in places like websites, forums, and e-mails, such places require good or bad intentions to distinguish between real and non-real identity use at the same time. This is sometimes for the solutions of crimes, sometimes for conceptional rights, sometimes for simply the name similarities. By examining a text that contains a crime element and by analyzing people's writing habits or styles (forms), author identificiation efforts help us to know about the true authors of those messages. In literature, author recognition is expressed as the process of determining the author of an article whose author is not known or whose author is suspected. Different works have been carried out on this field from day to day. Author recognition is considered as a classification problem and is expressed as the process of identifying the most appropriate author from the group of potential suspects. Within the scope of this thesis, it is aimed to develop author identification models in order to respond to different needs. Different tests have been carried out to assess the success of the models obtained. Accuracy values obtained from these tests vary between 99% and 74%. In addition, the success of the author features used in the author recognition study in determining the text type is evaluated and a different model for text type recognition is proposed. The proposed model shows whether a text belongs to the fields of 'Life', 'Politics' or 'Economy'. The accuracy of the proposed ANN (Artificial Neural Networks) models are between 88% and 70%. In this thesis, we also propose a hybrid ANN model which recognizes both writer and writing type in order to answer different needs and show the determination of the recommended author recognition and writing type recognition models.