Detecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approach
TABLE II: Results after tuning Naive Bayes w.r.t smoothing prior α for the features: n-gram range 1-3 and TFIDF normalization L2
['Alpha ( [ITALIC] α)', 'Accuracy']
We have considered the smoothing prior α for tuning. α≥0 considers the features which are not present in the training set and in turn prevents zero probabilities. Technically, α=1 is called Laplace smoothing and α<1 is called Lidstone smoothing. Naive Bayes performs better for the α value 0.1 giving 93.4% accuracy.