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
• 🌍 Multi-language support (English and Dutch)
• 📦 Batch processing capabilities
• 🛡️ Comprehensive error handling
• 🔄 Real-time sentiment analysis

Installation

pip install sentor-ml

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')

# Predict 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"
      ]
    }
  ]
# Predict with default language (English)
result = client.predict(input_data)
print(result)

# Predict with Dutch language
result_nl = client.predict(input_data, language="nl")
print(result_nl)

Language Support

The SDK supports multi-language sentiment analysis with the following options:

• "en" (default): English language prediction
• "nl": Dutch language prediction

# Default English prediction
result_en = client.predict(documents)

# Explicitly specify English
result_en = client.predict(documents, language="en")

# Dutch language prediction
result_nl = client.predict(documents, language="nl")

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
          }
        }
      ]
    }
  ]
}

Document Clustering

# Prepare documents for clustering (minimum 5 required)
documents = [
    {
        "doc_id": "doc1",
        "text": "Apple announced new iPhone features with improved camera.",
        "entities": ["Apple", "iPhone", "camera"]
    },
    {
        "doc_id": "doc2",
        "text": "Samsung launched Galaxy with advanced AI capabilities.",
        "entities": ["Samsung", "Galaxy", "AI"]
    },
    {
        "doc_id": "doc3",
        "text": "Apple plans to integrate AI into iOS ecosystem.",
        "entities": ["Apple", "AI", "iOS"]
    },
    {
        "doc_id": "doc4",
        "text": "SpaceX successfully launched Starlink satellites.",
        "entities": ["SpaceX", "Starlink", "satellites"]
    },
    {
        "doc_id": "doc5",
        "text": "Bitcoin price surged after ETF approval.",
        "entities": ["Bitcoin", "ETF"]
    }
]

# Cluster documents
clustering_result = client.cluster(documents, language='en')
print(f"Total clusters: {clustering_result['total_clusters']}")

Generating Topic Names

# After clustering, generate topic names for each cluster
for cluster in clustering_result['clusters']:
    topic_result = client.generate_topic_name(
        cluster_id=cluster['cluster_id'],
        documents=cluster['documents'],
        entities=cluster['entities'],
        top_words=cluster['top_words'],
        language='en'
    )
    
    print(f"Cluster {cluster['cluster_id']}: {topic_result['topic_name']}")

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.