Sentiment Analysis Tool

This project is a Sentiment Analysis Tool that classifies text into positive or negative sentiment using machine learning. It provides a RESTful API built with Flask and uses scikit-learn for model training.

Introduction

Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine whether data is positive, negative, or neutral. This tool leverages machine learning to analyze the sentiment of text data.

Prerequisites

• Python 3.10
• Virtual Environment (venv)
• Kaggle account with API credentials

Setup

1. Clone the repository:

   git clone https://github.com/eatwhatyoukill/sentiment-analysis.git

2. Create and activate a virtual environment:

   python -m venv venv
   venv\Scripts\activate  # For Windows
   source venv/bin/activate  # For Linux/Mac

3. Install the required Python packages using pip:

   pip install -r requirements.txt

4. Set up Kaggle API credentials:

   • Ensure you have your Kaggle API key saved in a kaggle.json file in the .kaggle directory within your home directory.

Usage

To run the application, use the following command:

python app.py

You can send a POST request to the /predict endpoint with the text you want to analyze. For example, using curl:

curl -X POST -H "Content-Type: application/json" -d "{\"text\": \"I love this product!\"}" http://127.0.0.1:5000/predict

This will return a JSON response indicating whether the sentiment is positive or negative.

Features

• Text Classification: Classifies text into positive or negative sentiment.
• RESTful API: Provides a RESTful API using Flask.
• Model Training: Trains a machine learning model using scikit-learn.

Technologies Used

• Python: The primary programming language used for development.
• Flask: A lightweight WSGI web application framework used to create the API.
• scikit-learn: A machine learning library used to build and train the sentiment analysis model.
• joblib: Used for model serialization and deserialization.
• Kaggle API: Used to download the dataset from Kaggle.

Azure Deployment

This project is configured to be deployed on Azure App Service using GitHub Actions.

GitHub Actions Workflow

The GitHub Actions workflow file is located at .github/workflows/main_sentimentAnalysis30081996.yml. It defines the steps to build and deploy the application to Azure.

Secrets

Ensure the following secrets are added to your GitHub repository:

• AZUREAPPSERVICE_CLIENTID
• AZUREAPPSERVICE_TENANTID
• AZUREAPPSERVICE_SUBSCRIPTIONID

Deploying to Azure

1. Commit and push your changes to the main branch.
2. The GitHub Actions workflow will automatically trigger and deploy the application to Azure.

Contributing

Report an Issue or Request a Feature

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

• The dataset used in this project is sourced from Kaggle.
• Special thanks to the contributors of the libraries and tools used in this project.

Contact

If you have any questions or suggestions, feel free to reach out.