Detection of Offensive Language in Social Networks Using LSTM and BERT Model

The uses of offensive languages on social media's like Instagram, Facebook, Twitter, etc. are increased tremendously. Nowadays, huge growth in the number of social media users are seen. Peoples are knowingly or unknowingly flooded the social media platforms with offensive posts. This become very challenging task to detect offensive posts. Manually solving this problem is not feasible, therefore automatic detection of offensive language is needed. Our objective of the work is to detect offensive language with the highest accuracy. The Davidson dataset is used for experiments with annotated tweets and implemented based on LSTM and BERT methods. The proposed deep learning methods is compared with other known machine learning classifier. Overall, result analysis is show that proposed deep learning method is outperforming others.