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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.
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