Shynar Mussiraliyeva1, Batyrkhan Omarov1,*, Paul Yoo1,2, Milana Bolatbek1
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 915-934, 2022, DOI:10.32604/cmc.2022.019189
Abstract In this research paper, we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus, thereby checking whether it is possible to detect extremist messages in the Kazakh language. To do this, the authors trained models using six classic machine-learning algorithms such as Support Vector Machine, Decision Tree, Random Forest, K Nearest Neighbors, Naive Bayes, and Logistic Regression. To increase the accuracy of detecting extremist texts, we used various characteristics such as Statistical Features, TF-IDF, POS, LIWC, and… More >
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