Performance Analysis of Artificial Neural Network Based Classfiers for Cyberbulling Detection
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Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, analyzes were performed to detection of cyberbullying by Artificial Neural Network (ANN) based classifiers. In contrast to the general classifiers used in the detection of cyberbullying in the literature, ANN basis classifiers as Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Radial Basis Function (RBF) and Logistic Regression (LR) classifiers have been tested. The performances of the classifiers mentioned in the study were tested with comments from Formspring.me and Myspace media. N-gram model was used for the qualitative derivation and N = 1 was chosen because we wanted to measure the overall performance of the classifiers, also stop-words have been removed from features. In these studies, the F-measure value was taken over than 0.90. Given the accuracy and time performance of the classifiers, it has been observed that the most appropriate classifier for cyberbullying detection is the SGD classifier. © 2018 IEEE.
Açıklama
3rd International Conference on Computer Science and Engineering, UBMK 2018 -- 20 September 2018 through 23 September 2018 -- Sarajevo -- 143560
Anahtar Kelimeler
Classification, Cyberbullying, DVM, Logistic Regression, RBF, SGD, Social Media, Text Mining
Kaynak
UBMK 2018 - 3rd International Conference on Computer Science and Engineering
