A Python SDK for interacting with the Sentor ML API for sentiment analysis
Project description
Sentor Python SDK
A Python SDK for interacting with the Sentor ML API for sentiment analysis. This SDK provides a simple and intuitive interface for sentiment analysis operations.
Features
- 🚀 Python 3.7+ support
- ⚡ Simple and intuitive API
- 🌍 Support for multiple languages
- 📦 Batch processing capabilities
- 🛡️ Comprehensive error handling
- 🔄 Real-time sentiment analysis
Installation
pip install sentor-ml
Get API key
Try with guest API key
Get a guest API key to try the API.
curl -X 'POST' \
'https://ml.sentor.app/api/auth/guest-access' \
-H 'accept: application/json' \
-d ''
Work like a PRO
- Go to Sentor ML API
- Subscribe to the Starter plan
- Get your API key
Usage
Basic Usage
from sentor import SentorClient
# Initialize the client
client = SentorClient('your-api-key')
# Analyze sentiment
input_data = [
{
"doc": "Apple's new iPhone is amazing!",
"doc_id": "1",
"entities": [
"Apple",
"iPhone"
]
},
{
"doc": "Samsung's new phone is amazing!",
"doc_id": "2",
"entities": [
"Samsung",
"phone"
]
}
]
result = client.analyze(input_data)
print(result)
Sample Output
{
"results": [
{
"doc_id": "1",
"predicted_class": 2,
"predicted_label": "positive",
"probabilities": {
"negative": 0.00010637386003509164,
"neutral": 0.0002509312762413174,
"positive": 0.9996427297592163
}
},
{
"doc_id": "2",
"predicted_class": 2,
"predicted_label": "positive",
"probabilities": {
"negative": 0.00010637386003509164,
"neutral": 0.0002509312762413174,
"positive": 0.9996427297592163
}
}
]
}
API Reference
Please refer to the Sentor ML API Documentation for more details. You can also try the API in the Sentor ML API Swagger Playground.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
MIT License - see the LICENSE file for details.
Project details
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