AI powered Cyberbullying Detection Model
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AI powered Cyberbullying Detection Model
Mr. Prasath B1, Sharmila S2, Vishnu Harsha N B3, and Yamini Durga K R4
1Assistant Professor (Sr.G), Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, e-mail: prasath.b@kpriet.ac.in
2Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, e-mail: sharmichandra04@gmail.com
3Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, e-mail: vishnuharsha.n.b@gmail.com
4Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and
Technology, e-mail: ramaryamini@gmail.com
ABSTRACT
Cyberbullying has arisen as an unavoidable and concerning issue via virtual entertainment stages, influencing the psychological well-being and prosperity of people around the world. To resolve this issue, this study proposes a cyberbullying recognition framework utilizing the K-SVM calculation. Utilizing the force of AI, the framework means to consequently distinguish and signal occurrences of cyberbullying progressively web-based entertainment content. The improvement of the location framework starts with the assortment and naming of a thorough dataset containing instances of cyberbullying and non-cyberbullying posts or remarks. After pre-handling the text information by eliminating unessential data, changing message over completely to lowercase, and tokenizing it, significant highlights are removed utilizing the pack of-words or TF-IDF methods. These changed element vectors act as contributions for preparing the K-SVM classifier, which tries to find the ideal hyper plane for successfully recognizing cyberbullying from non-cyberbullying content. The exhibition of the K-SVM model is assessed utilizing a different testing dataset, with measurements, for example, exactness, accuracy, review, F1-score, and ROC-AUC broke down to survey its viability in distinguishing cyberbullying cases. Model calibrating is led through trial and error with different K-SVM hyper boundaries and cross-approval methods to upgrade the framework's exhibition.
Keywords: Cyberbullying, social media, Online harassment
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