Detecting Hate Speech and Offensive Language on Twitter using Machine Learning

Toxic on-line content has become a serious issue in today's world because of Associate in Nursing exponential increase within the use of net by folks of various cultures and academic background. Differentiating hate speech and offensive language could be a key challenge in automatic detection of virulent text content. During this paper, we tend to propose Associate in Nursing approach to mechanically classify tweets on Twitter into 3 classes: hateful, offensive and clean. Victimization Twitter dataset, we tend to perform experiments considering n-grams as options and spending their term frequency-inverse document frequency (TFIDF) values to multiple machine learning models. We tend to perform comparative analysis of the models considering many values of n in n-grams and TFIDF normalisation strategies. When standardization the model giving the most effective results, we tend to accomplish ninety five.6% accuracy upon evaluating it on take a look at knowledge. we tend to conjointly produce a module that is Associate in Nursing intermediate between user and Twitter.