EXTREMISM DETECTION ON SOCIAL MEDIA USING SVM TEXT CLASSIFIER

Authors

  • Vijay , Dr. Pushpneel Verma

DOI:

https://doi.org/10.47750/vahhd779

Abstract

Spread of extremism on social media is major issue nowadays. Extremism can be expressed in the form of hate speech. It is necessary to distinguish hate speech from offensive language. In text documents hate speech can be detected by text classification. Text classifiers based on supervised machine learning can be used for hate speech detection. In this paper we discussed a method for hate speech detection by performing text classification with SVM. The dataset we used for experiments contained text tweets having hate speech or offensive language or neither hate speech nor offensive language. We used SVM classifiers with different kernels i.e. linear, sigmoid and RBF kernel. The highest classification accuracy was delivered by SVM with RBF kernel i.e. 89.04%.

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Published

2021-05-13

Issue

Section

Articles

How to Cite

EXTREMISM DETECTION ON SOCIAL MEDIA USING SVM TEXT CLASSIFIER. (2021). Journal of Pharmaceutical Negative Results, 12(1), 126-131. https://doi.org/10.47750/vahhd779