Hate Speech Detection using Deep Learning

With the exponential growth of social media platforms, the proliferation of hate speech has become a significant societal problem. Manual moderation of this vast amount of user-generated content is impractical, necessitating the development of automated detection systems. This research presents a deep learning-based approach for detecting hate speech in text data. We leverage a dataset of tweets labeled as hate speech, offensive language, or neither. Several deep learning models are explored, including Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM), to effectively classify the text. The methodology involves crucial preprocessing steps such as text cleaning, tokenization, and sequence padding to prepare the data for the models. Our experimental results demonstrate that the Bidirectional LSTM model achieves a high accuracy of 95.83% on the test set, outperforming other models. This study underscores the potential of deep learning techniques to create robust and scalable solutions for combating the spread of hate speech online.