This repository contains an end-to-end implementation of a Natural Language Processing (NLP) pipeline for hate speech classification. It demonstrates how to preprocess text data, extract features, train machine learning models, and evaluate their performance. The project aims to classify text data into hate speech or non-hate speech categories, providing an effective solution for tackling harmful content online.
Hate speech detection is an essential task for moderating online content and ensuring safer communication platforms. This project focuses on building a scalable, modular, and reproducible pipeline to classify text data using modern NLP techniques and machine learning models.
- Text Preprocessing: Cleaning and preparing raw text data for analysis.
- Feature Extraction: Implementing techniques like TF-IDF and word embeddings for text vectorization.
- Model Training: Experimenting with various machine learning algorithms to identify the best-performing model.
- Evaluation Metrics: Using metrics such as accuracy, precision, recall, F1-score, and confusion matrix to assess model performance.
- Scalable Pipeline: Modular code structure for easy integration and reproducibility.
This project is applicable in various domains, including:
- Social Media Moderation: Identifying and flagging harmful or abusive content.
- Content Filtering: Ensuring safer communication platforms by detecting hate speech.
- Sentiment Analysis: Expanding into broader sentiment analysis tasks beyond hate speech detection.
- The datasets used in this project (e.g., Kaggle Hate Speech Dataset, Twitter Hate Speech Dataset).
- The authors and contributors of open-source libraries used in this project.
This project is licensed under the MIT License. See the LICENSE file for more details.
For any questions or suggestions, please feel free to open an issue in the GitHub repository.