🗨️ Multi-Label-Toxic-Comment-Detection-With-Deep-Learning 🤖🛡️

Multi-Label-Toxic-Comment-Detection-With-Deep-Learning is an NLP project designed to automatically detect and classify toxic comments into multiple categories such as toxic, severe toxic, obscene, threat, insult, and identity hate. Using deep learning models, it helps build safer and more inclusive online communities by filtering harmful language in real time.

✨ Key Features

📂 Dataset – Works with Kaggle’s Toxic Comment Classification dataset (Wikipedia comments)

🧹 Text Preprocessing – Tokenization, stopword removal, lemmatization, embeddings

🔤 Word Embeddings – GloVe / FastText / Word2Vec for semantic representation

🧠 Deep Learning Models – BiLSTM, GRU, CNN, and Transformer-based architectures

🎯 Multi-Label Classification – One comment can belong to multiple toxic categories

📊 Evaluation Metrics – Precision, Recall, F1-score, ROC-AUC for each class

🖼️ Visualization – Word clouds, confusion matrix, and label distribution

🌐 Deployment (Optional) – Streamlit / Flask app for live toxic comment detection

🧰 Tech Stack

Programming: Python 🐍

Libraries: Pandas, NumPy, Matplotlib, Seaborn, NLTK, SpaCy

Deep Learning: TensorFlow / Keras, PyTorch

Embeddings: GloVe, FastText, Word2Vec

Deployment (Optional): Streamlit / Flask

📁 Project Structure 📁 data/ # Toxic comment dataset 📁 notebooks/ # Jupyter notebooks for preprocessing & modeling 📁 src/ # Scripts for training & evaluation 📁 models/ # Trained deep learning models 📁 results/ # Metrics, confusion matrix, and visualizations 📁 app/ # (Optional) Web app for real-time detection

🚀 Getting Started git clone https://github.com/yourusername/Multi-Label-Toxic-Comment-Detection-With-Deep-Learning.git cd Multi-Label-Toxic-Comment-Detection-With-Deep-Learning pip install -r requirements.txt jupyter notebook

📌 Use Cases

🛡️ Content Moderation – Automatically flag offensive or harmful comments

🌍 Social Media Platforms – Improve community guidelines enforcement

📰 News & Blogs – Maintain respectful discussions in comment sections

🎓 Research – Explore multi-label NLP and deep learning classification

🤝 Contributing

Contributions are welcome! Enhance preprocessing, add new model architectures, or improve deployment and submit a PR.

📜 License

MIT License – Free for academic, research, and personal use.