ABHORRENT TWEET TEXT DETECTION USING ENSEMBLE OF CLASSIFIERS METHODS

Nowadays Abhorrent Tweet on social media has become a major problem. Abhorrent tweet may cause many serious and negative mental, emotional and physical impacts on a person's life. However, abhorrence leaves a record that can demonstrate value and give proof to help stop digital abuse. The early detection of abhorrent tweet on social media becomes crucial to moving the effect on the social media user. Numerous studies are being done to automatically identify cyberbullying content in this trend. The absence of linguistic resources, especially for recently developed languages, is the main issue and gap in Abhorrent/Cyberbullying detection measures. Using Machine Learning with Natural Language Processing (NLP) techniques to automatically detect cyberbullying is the best way to stop it. Current research develops an efficient framework to detect Cyberbullying, using NLP tools with Machine Learning and Ensemble models. Using different preprocessing techniques, the proposed study is validated on an english-abusive-comment-detector. Five machine learning models Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) and their different combination (Ensembles) are evaluated on the different dataset. From experiments, the current study finds that the Ensemble model outperformed and achieved promising results from individual models. In last, an ensemble of these outperformed models is formed and achieved higher test accuracy.