DEEP LEARNING BASED FUSION APPROACH FOR HATE SPEECH DETECTION

Project Algorithm :

NLP,CNN

Project Overview :

Hate speech on online platforms poses serious risks by promoting discrimination, harassment, and violence. Traditional keyword-based or rule-based systems are limited, as users often obfuscate offensive words (e.g., “1di0t”), mix languages, or use indirect expressions. To address this, the project proposes a deep learning-based fusion model that combines multiple textual representations—transformer-based embeddings, character-level encodings, and handcrafted linguistic features—to improve robustness and accuracy in hate speech detection. The system applies Natural Language Processing (NLP) and deep learning fusion techniques to classify text into categories such as hate speech, offensive, or normal. The model is deployed as a web application, providing a user-friendly interface where users can input text and receive real-time detection results. This ensures more effective content moderation, safer digital spaces, and support for multi-lingual or code-mixed data.

Reference Video : -