The Toxic Comment Classifier is a machine learning project designed to identify and classify toxic comments in online discussions. This tool aims to improve online communication by detecting and flagging toxic content such as insults, threats, and hate speech, thereby promoting a healthier and more respectful online environment.
- Data Collection: Gather a dataset of online comments labeled with categories such as toxic, severe toxic, obscene, threat, insult, and identity hate.
- Data Preprocessing: Clean and preprocess the text data to prepare it for training.
- Model Selection: Choose appropriate machine learning models for text classification.
- Model Training: Train the models on the preprocessed dataset.
- Model Evaluation: Evaluate the performance of the models using appropriate metrics.
- Deployment: Deploy the best-performing model as a web service for real-time comment classification.
The dataset used for this project can be sourced from platforms such as Kaggle, where there are publicly available datasets for toxic comment classification. An example dataset is the "Toxic Comment Classification Challenge" dataset from Kaggle.
- Programming Language: Python
- Libraries:
- Text Processing: NLTK, spaCy, re
- Machine Learning: scikit-learn, TensorFlow, Keras, PyTorch
- Data Manipulation: pandas, numpy
- Development Environment: Jupyter Notebook, Google Colab
- Deployment: Flask/Django for the web service, Docker for containerization, AWS/GCP for cloud deployment