Classification of hate speech in social media using Machine Learning and Natural Language Processing

This study aims to apply Natural Language Processing (NLP) and develop Machine Learning algorithms for detecting hate speech in social media. The “Hate Speech and Offensive Language dataset” is used, which comprises 24,783 manually annotated English tweets labelled as hate speech, offensive language, or neither. Random Forest Classifier, RNN, LSTM, and Bi-LSTM models are evaluated for classifying hate speech and recommending effective strategies for minimising offensive language on social media platforms. Model Comparison helps to understand the best-fitted model in the context of predicting and classifying hate speech in social media platforms. All the objectives of this research are achieved through the entire data analysis, from data pre-processing to the evaluation of different ML techniques. More Neural Network models and hyperparameter-tuned ML-models would enhance the model performance in classifying hate speech in the future.