Classification of Cyberbullying Romanized Sinhala Comments in Online Platforms

Abstract

"This study investigates the detection of cyberbullying in Romanized Sinhala using various machine learning classifiers and feature extraction methods. The primary objective is to identify the most effective combination of classifier and feature extraction techniques for this task. We employ rule-based, Bag-of-Words (BoW), and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction methods, as well as additional features such as word count and gender. The classifiers studied include K-Nearest Neighbours (KNN), Voting, Random Forest, Support Vector Machines (SVM), Decision Tree, Naive Bayes, Multilayer Perceptron (MLP), AdaBoost, and Logistic Regression."