Sentiment Analysis Web Application

Welcome to the wiki for the Sentiment Analysis Web Application! This wiki provides detailed documentation about the project, its features, setup instructions, and usage examples.

Overview

This web application analyzes the sentiment of text using machine learning techniques. It provides both a user-friendly web interface and a RESTful API for integration with other systems.

Quick Links

• Installation Guide
• Usage Guide
• API Documentation
• Model Information
• Contributing Guidelines
• Troubleshooting

Features

• Text sentiment analysis (Positive, Negative, Neutral)
• Confidence score for predictions
• History of recent analyses
• RESTful API for integration
• Responsive web interface

Technology Stack

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript, Bootstrap
• Machine Learning: scikit-learn, NLTK
• Production Server: Waitress

Project Structure

sentiment-analysis/
├── app.py                 # Main Flask application
├── run.py                 # Production server script
├── config.py              # Configuration settings
├── sentiment_model.pkl    # Trained ML model
├── tfidf_vectorizer.pkl   # TF-IDF vectorizer
├── model.ipynb            # Jupyter notebook for model training
├── templates/
│   └── index.html         # Web interface template
└── README.md              # Project overview

License

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