Abstract
Nowadays, on the internet, something unusual is happening. Abusive behaviors towards people on social media, as well as these online abominations, have real-life implications, such as the spread of fear and hatred through comments. Trolls on the internet tend to use derogatory language. In a nutshell, online prejudice has aided the spread of hatred. Toxic words are only effective because they can implant discriminatory beliefs without being noticed. To solve this problem, a Machine Learning-Based Tool to Classify Online Toxic Comment is proposed which uses seven machine learning algorithms, including Random Forest, KNN, SVM, Logistic Regression, Decision Tree, Naive Bayes, and Hybrid Algorithm, and apply them to input data to solve the problem of text classification and identify the best machine learning algorithm by using evaluation metrics for toxic comments classification by the accuracy comparison graph. The proposed model also predicts whether the given input comment is Toxic or Non-Toxic.
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