The Effects of Attribute Selection in Artificial Neural Network Based Classifiers on Cyberbullying 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
Recently, as a result of the rapid increase of information communication technologies, the use of smartphones, tablets and laptop computers has become widespread. Especially among young people, social networks have become a part of everyday life and the cyberbullying problem has arisen as a result of hiding the credentials of people in cyberspace and reaching every level. In this study was carried out assays for the detection of cyberbullying. A total of 3469 reviews from Youtube were tagged as positive and negative, depending on whether have contained bullying. In the analyzes, the number of features of the data set was reduced to 10, 50, 100, 250 and 500 using the minimum redundancy and maximum relevance (MRMR), ReliefF and recursive feature elimination (RFE) algorithms as feature selection algorithms, support vector machines (SVM), stochastic gradient descent (SGD), radial basis function (RBF) and logistic regression (LR) have been preferred as classification algorithms. As a result of the experimental studies, the use of the SGD classifier together with the RFE attribute selection algorithm resulted 0.943 F-measure value. Other quality selection algorithms did not produce as high values as RFE, but F-measure values of about 0.76 to 0.84 were obtained. © 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, MRMR, RBF, ReliefF, RFE, SGD, Social Media, Text Minning
Kaynak
UBMK 2018 - 3rd International Conference on Computer Science and Engineering
