Detection of Cyberbullying using Voting Classifier - Kaur, R; Sagar, MS (International Journal of Computer Sciences and Engineering, 2020)
The advent of social media has changed the ways of human communication. It has brought people around the world closer to each other. Despite its innumerable benefits, social media is considered to be one of the harmful elements of society. Cyberbullying and online harassment are the most common negative effects of social media. Cyberbullying is a way of bullying someone with the use of technology and it can take place through many forms such as SMS, Apps, online gaming, social networking sites online forums, etc. The project aims at detecting cyberbullying content based on textual features. The system detects various language patterns often used by bullies. This is accomplished using machine learning. The proposed system uses voting classifier to classify the input text as „Bullying‟ or „Non-Bullying‟. It also compares the accuracies of various classifiers and introduces a framework of supervised machine learning to detect cyberbullying in textual data. It is observed that a voting classifier i.e. a combination of the Logistic Regression, Random Forest, Support Vector Machine, SGD classifier gives the highest accuracy and precision i.e. 74% and 77% respectively. This trained model is deployed on a webpage which makes the system user intuitive and user-friendly.