Danitilahun/Amharic_NLP_Sentiment_Analysis_Neural_Networks_LSTM

This project is designed to perform sentiment analysis on Amharic text using a neural network model. The model is built using PyTorch for the machine learning component and Flask for the web interface. The project includes preprocessing of Amharic text, training a sentiment analysis model. # Amharic Sentiment Analysis using Neural Networks This project is designed to perform sentiment analysis on Amharic text using a neural network model. The model is built using **PyTorch** for the machine learning component and **Flask** for the web interface. The project includes preprocessing of Amharic text, training a sentiment analysis model, and deploying it as a web application. ## Table of Contents - Installation - Usage - Model Performance - API Endpoints - Dependencies - Contributing - License --- ## Installation 1. **Clone the repository:** ```bash git clone github.com ``` 2. **Create a virtual environment:** ```bash python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate` ``` 3. **Install dependencies:** ```bash pip install -r requirements.txt ``` --- ## Usage 1. **Run the Flask application:** ```bash flask run --port=5001 ``` 2. **Access the web interface:** Open your browser and go to `127.0.0.1`. 3. **Analyze sentiment:** Enter Amharic text in the provided input field and submit to get the sentiment analysis result. --- ## Model Performance The model's performance on the **train** and **test** datasets is as follows: ### Train Metrics: - **Accuracy**: 0.9461 - **Precision**: 0.9757 - **Recall**: 0.9185 - **F1 Score**: 0.9464 - **AUC**: 0.9781 ### Test Metrics: - **Accuracy**: 0.9219 - **Precision**: 0.9522 - **Recall**: 0.8937 - **F1 Score**: 0.9220 - **AUC**: 0.9575 ### Classification Report (Train): - **Class 0.0**: - Precision: 0.9179 - Recall: 0.9754 - F1-Score: 0.9458 - Support: 26757.0 - **Class 1.0**: - Precision: 0.9756 - Recall: 0.9188 - F1-Score: *Missing* - Support: *Missing* ### Classification Report (Test): - **Class 0.0**: - Precision: 0.8939 - Recall: 0.9529 - F1-Score: 0.9218 - Support: 2983.0 - **Class 1.0**: - Precision: 0.9523 - Recall: 0.8937 - F1-Score: *Missing* - Support: *Missing* --- ## AP …