Profanity Detector on Twitter Using Naive Bayes and Decision Tree

In modern times, profanities can be found all over the internet including social media platforms like Twitter. The study will compare the performance of Naive Bayes and Decision Tree algorithms, which are widely used in text classification tasks. The dataset for training and testing the profanity detector will consist of an extensive collection of tweets labeled as offensive or not offensive. The dataset will be preprocessed to be used to train the models of classification. The Decision Tree achieves an accuracy of 0.94, while Multinomial Naïve Bayes achieves an 0.92 accuracy.