README.md

metadata

title: Sentiment Analysis
emoji: 🐨
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
short_description: Sentiment analysis MCP server using Gradio.

Sentiment Analysis MCP

A Python-based sentiment analysis application using TextBlob and Gradio that provides real-time sentiment analysis through a web interface.

Features

• Real-time sentiment analysis using TextBlob's natural language processing
• Interactive web interface powered by Gradio
• Comprehensive metrics including polarity, subjectivity, and sentiment assessment
• MCP server integration for enhanced functionality
• Easy-to-use interface with instant results

What It Analyzes

• Polarity: Sentiment score from -1.0 (very negative) to +1.0 (very positive)
• Subjectivity: Objectivity score from 0.0 (objective) to 1.0 (subjective)
• Assessment: Simple classification as "positive", "negative", or "neutral"

Installation

Prerequisites

• Python 3.7 or higher
• pip package manager

Setup

1. Clone the repository:

   git clone https://github.com/GitBuntu/sentiment-analysis-mcp.git
   cd sentiment-analysis-mcp
   

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

Usage

Running the Application

1. Start the application:

   python app.py
   

2. Open your web browser and navigate to the local URL displayed in the terminal (typically http://127.0.0.1:7860)

3. Enter text in the input box and get instant sentiment analysis results

Example Results

Input: "The food was delicious but the service was slow and disappointing."

Output:

{
  "polarity": 0.1,
  "subjectivity": 0.9,
  "assessment": "positive"
}

MCP Server Configuration

SSE Support

Some MCP clients don't support SSE-based MCP Servers. In those cases, use mcp-remote:

{
  "mcpServers": {
    "gradio": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "http://localhost:7860/gradio_api/mcp/sse"
      ]
    }
  }
}

Connection Issues

If you encounter connection problems:

• Try restarting both the client and server
• Check that the server is running and accessible
• Verify that the MCP schema is available at the expected URL

Testing

The project includes a comprehensive test file (test_texts.txt) with examples for:

• Positive sentiment - Happy, excited, satisfied expressions
• Negative sentiment - Angry, disappointed, frustrated expressions
• Neutral sentiment - Factual, objective statements
• Mixed sentiment - Texts with both positive and negative elements
• Highly subjective - Personal opinions and emotional expressions
• Objective statements - Factual information with minimal emotion
• Edge cases - Empty strings, punctuation, negations, ambiguous text

Sample Test Cases

Text	Expected Polarity	Expected Assessment
"I absolutely love this new restaurant!"	High positive (~0.8)	positive
"This product is terrible."	High negative (~-0.8)	negative
"The meeting is at 3 PM."	Near zero (~0.0)	neutral
"The food was delicious but service was slow."	Slightly positive	positive

Project Structure

sentiment-analysis-mcp/
├── app.py              # Main application file
├── requirements.txt    # Python dependencies
├── test_texts.txt     # Test cases for sentiment analysis
├── README.md          # Project documentation
└── venv/              # Virtual environment (created after setup)

Dependencies

• textblob>=0.17.1 - Natural language processing library for sentiment analysis
• gradio>=4.0.0 - Web interface framework for machine learning applications

How It Works

1. Text Processing: Input text is processed using TextBlob's natural language processing capabilities
2. Sentiment Calculation: TextBlob calculates polarity and subjectivity scores
3. Classification: The application categorizes sentiment as positive, negative, or neutral based on polarity
4. Web Interface: Gradio provides an interactive web interface for easy testing and usage

Use Cases

• Social media monitoring - Analyze customer feedback and comments
• Product reviews - Understand customer satisfaction from reviews
• Survey analysis - Process open-ended survey responses
• Content moderation - Identify potentially negative content
• Market research - Analyze public opinion on topics or products

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is open source and available under the MIT License.

Limitations

• Language Support: Currently optimized for English text
• Context Understanding: May not fully understand complex sarcasm or context-dependent sentiment
• Domain Specificity: General-purpose model may not be optimized for specific domains
• Sentence Length: Very short texts may not provide reliable sentiment scores

Future Enhancements

• Support for multiple languages
• Custom model training capabilities
• Batch processing for multiple texts
• Export results to CSV/JSON
• REST API endpoints
• Docker containerization
• Advanced visualization charts

Support

If you encounter any issues or have questions:

1. Check the test_texts.txt file for examples
2. Review the error messages in the terminal
3. Open an issue on GitHub with detailed information about the problem

Acknowledgments

• TextBlob - For providing excellent natural language processing capabilities
• Gradio - For the easy-to-use web interface framework
• Python Community - For the amazing ecosystem of tools and libraries