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Toxic online speech has become a crucial problem nowadays due to anexponential increase in the use of internet by people from different culturesand educational backgrounds. Differentiating if a text message belongs to hatespeech and offensive language is a key challenge in automatic detection oftoxic text content. In this paper, we propose an approach to automaticallyclassify tweets into three classes: Hate, offensive and Neither. Using publictweet data set, we first perform experiments to build BI-LSTM models from emptyembedding and then we also try the same neural network architecture withpre-trained Glove embedding. Next, we introduce a transfer learning approachfor hate speech detection using an existing pre-trained language model BERT(Bidirectional Encoder Representations from Transformers), DistilBert(Distilled version of BERT) and GPT-2 (Generative Pre-Training). We performhyper parameters tuning analysis of our best model (BI-LSTM) consideringdifferent neural network architectures, learn-ratings and normalization methodsetc. After tuning the model and with the best combination of parameters, weachieve over 92 percent accuracy upon evaluating it on test data. We alsocreate a class module which contains main functionality including textclassification, sentiment checking and text data augmentation. This model couldserve as an intermediate module between user and Twitter.

Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning