sentor-ml 1.0.2

A Python SDK for interacting with the Sentor ML API for sentiment analysis

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

1. Go to Sentor ML API
2. Subscribe to the Starter plan
3. Get your API key

Usage

Basic Usage

from sentor import SentorClient

# Initialize the client
client = SentorClient('your-api-key')

# Analyze sentiment
input_data = [
    {
      "doc": "In the competitive landscape of consumer electronics, Apple and Samsung continue to lead the market with innovative products and strong brand loyalty. While Apple focuses on a tightly integrated ecosystem with devices like the iPhone, iPad, and Mac, Samsung excels in offering a wide range of options across various price points, especially in its Galaxy smartphone lineup. Both companies push the boundaries of technology, from cutting-edge chipsets to advanced camera systems, often setting industry trends that others follow.",
      "doc_id": "0",
      "entities": [
        "Apple",
        "Samsung",
        "camera"
      ]
    },
    {
      "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": "0",
      "predicted_class": 2,
      "predicted_label": "positive",
      "probabilities": {
        "negative": 0.00007679959526285529,
        "neutral": 0.0002924697764683515,
        "positive": 0.9996306896209717
      },
      "details": [
        {
          "sentence_index": 0,
          "sentence_text": "In the competitive landscape of consumer electronics, Apple and Samsung continue to lead the market with innovative products and strong brand loyalty.",
          "predicted_class": 2,
          "predicted_label": "positive",
          "probabilities": {
            "negative": 0.00009389198385179043,
            "neutral": 0.00032428017584607005,
            "positive": 0.9995818734169006
          }
        },
        {
          "sentence_index": 1,
          "sentence_text": "While Apple focuses on a tightly integrated ecosystem with devices like the iPhone, iPad, and Mac, Samsung excels in offering a wide range of options across various price points, especially in its Galaxy smartphone lineup.",
          "predicted_class": 2,
          "predicted_label": "positive",
          "probabilities": {
            "negative": 0.00005746580063714646,
            "neutral": 0.00012963586777914315,
            "positive": 0.99981290102005
          }
        },
        {
          "sentence_index": 2,
          "sentence_text": "Both companies push the boundaries of technology, from cutting-edge chipsets to advanced camera systems, often setting industry trends that others follow.",
          "predicted_class": 2,
          "predicted_label": "positive",
          "probabilities": {
            "negative": 0.00006366783054545522,
            "neutral": 0.00044553453335538507,
            "positive": 0.9994907379150391
          }
        }
      ]
    },
    {
      "doc_id": "1",
      "predicted_class": 2,
      "predicted_label": "positive",
      "probabilities": {
        "negative": 0.00010637375817168504,
        "neutral": 0.0002509312762413174,
        "positive": 0.9996427297592163
      },
      "details": [
        {
          "sentence_index": 0,
          "sentence_text": "Apple's new iPhone is amazing!",
          "predicted_class": 2,
          "predicted_label": "positive",
          "probabilities": {
            "negative": 0.00010637375817168504,
            "neutral": 0.0002509312762413174,
            "positive": 0.9996427297592163
          }
        }
      ]
    },
    {
      "doc_id": "2",
      "predicted_class": 2,
      "predicted_label": "positive",
      "probabilities": {
        "negative": 0.00010637375817168504,
        "neutral": 0.0002509312762413174,
        "positive": 0.9996427297592163
      },
      "details": [
        {
          "sentence_index": 0,
          "sentence_text": "Samsung's new phone is amazing!",
          "predicted_class": 2,
          "predicted_label": "positive",
          "probabilities": {
            "negative": 0.00010637375817168504,
            "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.