Effects of Feature Extraction and Classification Methods on Cyberbully Detection

Selma Ayşe Özel; Esra Saraç

doi:10.19113/sdufbed.20964

Effects of Feature Extraction and Classification Methods on Cyberbully Detection

Abstract

Cyberbullying is defined as an aggressive, intentional action against a defenseless person by using the Internet, or other electronic contents. Researchers have found that many of the bullying cases have tragically ended in suicides; hence automatic detection of cyberbullying has become important. In this study we show the effects of feature extraction, feature selection, and classification methods that are used, on the performance of automatic detection of cyberbullying. To perform the experiments FormSpring.me dataset is used and the effects of preprocessing methods; several classifiers like C4.5, Naïve Bayes, kNN, and SVM; and information gain and chi square feature selection methods are investigated. Experimental results indicate that the best classification results are obtained when alphabetic tokenization, no stemming, and no stopwords removal are applied. Using feature selection also improves cyberbully detection performance. When classifiers are compared, C4.5 performs the best for the used dataset.

Keywords

Cyberbullying,Preprocessing; Feature selection; Classification

References

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Details

Primary Language

Turkish

Subjects

-

Journal Section

-

Authors

Selma Ayşe Özel

Esra Saraç This is me

Publication Date

April 15, 2017

Submission Date

June 14, 2016

Acceptance Date

-

Published in Issue

Year 2017 Volume: 21 Number: 1

DOI

https://doi.org/10.19113/sdufbed.20964

IZ

https://izlik.org/JA27RC82XG

Cite

RIS / Bibtex

APA

Özel, S. A., & Saraç, E. (2017). Effects of Feature Extraction and Classification Methods on Cyberbully Detection. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21(1), 190-200. https://doi.org/10.19113/sdufbed.20964

AMA

1.Özel SA, Saraç E. Effects of Feature Extraction and Classification Methods on Cyberbully Detection. J. Nat. Appl. Sci. 2017;21(1):190-200. doi:10.19113/sdufbed.20964

Chicago

Özel, Selma Ayşe, and Esra Saraç. 2017. “Effects of Feature Extraction and Classification Methods on Cyberbully Detection”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21 (1): 190-200. https://doi.org/10.19113/sdufbed.20964.

EndNote

Özel SA, Saraç E (April 1, 2017) Effects of Feature Extraction and Classification Methods on Cyberbully Detection. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21 1 190–200.

IEEE

[1]S. A. Özel and E. Saraç, “Effects of Feature Extraction and Classification Methods on Cyberbully Detection”, J. Nat. Appl. Sci., vol. 21, no. 1, pp. 190–200, Apr. 2017, doi: 10.19113/sdufbed.20964.

ISNAD

Özel, Selma Ayşe - Saraç, Esra. “Effects of Feature Extraction and Classification Methods on Cyberbully Detection”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21/1 (April 1, 2017): 190-200. https://doi.org/10.19113/sdufbed.20964.

JAMA

1.Özel SA, Saraç E. Effects of Feature Extraction and Classification Methods on Cyberbully Detection. J. Nat. Appl. Sci. 2017;21:190–200.

MLA

Özel, Selma Ayşe, and Esra Saraç. “Effects of Feature Extraction and Classification Methods on Cyberbully Detection”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 21, no. 1, Apr. 2017, pp. 190-0, doi:10.19113/sdufbed.20964.

Vancouver

1.Selma Ayşe Özel, Esra Saraç. Effects of Feature Extraction and Classification Methods on Cyberbully Detection. J. Nat. Appl. Sci. 2017 Apr. 1;21(1):190-20. doi:10.19113/sdufbed.20964

Effects of Feature Extraction and Classification Methods on Cyberbully Detection

Effects of Feature Extraction and Classification Methods on Cyberbully Detection

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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