---
title: MCP Sentiment Analysis Server
emoji: 🎭
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "5.0.0"
app_file: app.py
pinned: false
license: mit
---

# MCP Sentiment Analysis Server

This is a Model Context Protocol (MCP) server that provides sentiment analysis capabilities using an improved TextBlob-based approach with negation detection.

## Features

- **Comprehensive Sentiment Analysis**: Detailed sentiment analysis with polarity and subjectivity scores
- **Numerical Sentiment Scoring**: Simple numerical scoring from -1 (very negative) to 1 (very positive)
- **Emotion Classification**: Basic emotion classification (Joy, Sadness, Anger, etc.)
- **Batch Text Processing**: Analyze multiple texts at once with summary statistics
- **Improved Negation Handling**: Better detection of negated sentiments compared to vanilla TextBlob
- **Full MCP Protocol Support**: Compatible with MCP clients and AI applications

## Usage

### Web Interface
Use the interface above to test the sentiment analysis tools directly.

### MCP Endpoint
Connect your MCP clients to: `https://YOUR_USERNAME-SPACE_NAME.hf.space/gradio_api/mcp/sse`

### Available Tools
- `analyze_sentiment`: Comprehensive sentiment analysis with debug information
- `get_sentiment_score`: Numerical sentiment scoring (-1 to 1)
- `classify_emotion`: Basic emotion classification with confidence scores
- `batch_analyze`: Analyze multiple texts at once with summary statistics

## Example with smolagents

```python
from smolagents.mcp_client import MCPClient

with MCPClient(
    {"url": "https://YOUR_USERNAME-SPACE_NAME.hf.space/gradio_api/mcp/sse"}
) as tools:
    for tool in tools:
        print(f"{tool.name}: {tool.description}")
```

## Example with Cursor IDE

Add to your MCP configuration:

```json
{
  "mcpServers": {
    "sentiment-analysis": {
      "command": "npx",
      "args": [
        "-y", 
        "mcp-remote", 
        "https://YOUR_USERNAME-SPACE_NAME.hf.space/gradio_api/mcp/sse", 
        "--transport", 
        "sse-only"
      ]
    }
  }
}
```

## Improvements Over Standard TextBlob

This implementation includes several improvements over standard TextBlob sentiment analysis:

1. **Negation Detection**: Automatically detects common negation patterns (don't, can't, not, etc.)
2. **Sentiment Correction**: Corrects TextBlob's tendency to misclassify negated positive statements
3. **Conservative Thresholds**: Uses more conservative thresholds for sentiment classification
4. **Debug Information**: Provides transparency into the analysis process

## Example Results

**Input**: "I don't like this kind of things happen"
- **Standard TextBlob**: Positive (0.6) ❌
- **This Implementation**: Negative (-0.48) ✅

**Input**: "This is not good"
- **Standard TextBlob**: Positive (0.7) ❌  
- **This Implementation**: Negative (-0.35) ✅

## Technical Details

- Built with Gradio and TextBlob
- Uses regex-based negation detection
- Implements conservative sentiment thresholds
- Provides JSON-formatted responses
- Full MCP protocol compatibility via Server-Sent Events (SSE)

## License

MIT License - see the course repository for details.