dawit-melka/Amharic-Hate-speech-detection

# Amharic Hate Speech Detection ## Overview This project aims to develop a hate speech detection system for the Amharic language. Hate speech detection is crucial for maintaining a safe and respectful online environment, especially on social media platforms where hate speech can proliferate quickly. The system will analyze text data written in Amharic and classify it as either hate speech or non-hate speech. ## Dataset The dataset used for training and testing the hate speech detection model will consist of a large collection of text samples written in Amharic. This dataset will be annotated to label each text sample as either hate speech or non-hate speech. It's essential to ensure the dataset is diverse and representative of the various forms of hate speech present in online communication. ## Methodology The hate speech detection system will utilize natural language processing (NLP) techniques and machine learning algorithms to analyze text data written in Amharic. The methodology will involve the following steps: - Data preprocessing: Cleaning and preparing the text data for analysis. - Feature extraction: Extracting relevant features from the text data. - Model training: Training a machine learning model, such as a deep learning model or a traditional classifier, on the annotated dataset. - Evaluation: Evaluating the performance of the trained model using metrics such as accuracy, precision, recall, and F1-score. - Deployment: Deploying the trained model to classify new text data as hate speech or non-hate speech in real-time. ## Dependencies - Python 3.x - Libraries: TensorFlow, Keras, PyTorch, RNN, scikit-learn, pandas, numpy, etc.