Hate Speech Detection Using Deep learning

Hate speech has become a pressing issue in the age of digital communication. With the rise of social media and online platforms, detecting and controlling offensive content is crucial. This project presents a machine learning-based system to identify hate speech in text data. The system uses natural language processing (NLP) techniques to preprocess and analyze input text. A labeled dataset containing exam ples of hate and non-hate speech is used for training. Various models such as Logistic Regression, Naive Bayes, and Support Vector Machines are evaluated. Deep learning methods like LSTM and BERT are also explored for improved accuracy. Text features are extracted using techniques like TF-IDF and word embeddings. The trained model is integrated into a web-based interface for real-time predictions. Users can input text and receive immediate feedback on potential hate content. The system aims to assist platforms in moderating harmful speech effectively. Results show promising accuracy and reliability in detecting offensive content. This approach contributes to creating safer digital environments using AI.