How to Do Entity Sentiment Analysis Using Python

In this guide, we’ll show you how to do entity sentiment analysis using Python. Whether you're extracting insights from text, analyzing sentiment towards specific entities, or enhancing natural language processing tasks, this tutorial covers essential techniques for entity sentiment analysis.

By the end, you'll be able to accurately assess sentiment tied to entities, improving your text analysis capabilities in Python-based projects.

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What is Entity Sentiment Analysis?

Entity Sentiment Analysis is a natural language processing (NLP) technique that identifies entities (such as people, brands, locations, or products) in a text and determines the sentiment associated with each entity.

Instead of analyzing overall sentiment in a text, this method pinpoints how specific entities are perceived—positive, negative, or neutral—helping businesses and applications gain more precise insights.

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Use Cases of Entity Sentiment Analysis

1. Brand Monitoring – Companies can track public perception of their brand by analyzing social media, reviews, and news articles to understand sentiment toward their products or services.
2. Customer Feedback Analysis – Businesses can extract insights from customer reviews and support tickets to identify which products or features receive positive or negative sentiment.
3. Market Research – Analysts can assess sentiment trends toward competitors, industry trends, or new product launches to make data-driven business decisions.
4. Media & News Analysis – Journalists and researchers can track how people, companies, or political figures are being discussed in news articles and social media.

By implementing entity sentiment analysis, businesses and developers can gain deeper, actionable insights from textual data, improving decision-making and user experience.

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How to analyze Entity Sentiment?

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Set Up Your Eden AI Account

1. Sign Up: If you haven’t created an Eden AI account yet, you can quickly sign up for free using this link. our API key will be available in the API Keys section, along with free credits provided by Eden AI.

2. Access Text Processing Technologies: After logging in, navigate to the text processing section of the platform.

3. Select Entity Sentiment Analysis: Choose the Entity Sentiment Analysis feature.

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Implementing Entity Sentiment Analysis in Python

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Install Python’s Requests Module: To interact with the Eden AI API, ensure you have the requests module installed. If not, install it using:

pip install requests

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Prepare the Code

Below is a Python script that performs Entity Sentiment Analysis using Eden AI’s API.

    
import json
import requests

# Set authorization header
headers = {"Authorization": "Bearer YOUR_API_KEY"}

# Define the API endpoint and the request payload
url = "https://api.edenai.run/v2/text/entity_sentiment"
payload = {
    "providers": "google",
    "text": "Barack Hussein Obama is an American politician who served as the 44th president of the United States...",
    "language": "en",
}

# Make the API request
response = requests.post(url, json=payload, headers=headers)

# Parse the response
result = json.loads(response.text)

# Print each entity's sentiment and type
for item in result['google']['items']:
    print(f"Text: {item['text']}")
    print(f"Sentiment: {item['sentiment']}")
    print(f"Entity: {item['type']}\n")
    
  

• ‍Authentication: Uses the API key in the request headers.‍
• API Endpoint: Sends a request to Eden AI’s entity sentiment analysis endpoint.‍
• Payload: Includes the text to analyze and the chosen provider (Google NLP in this case).‍
• Response Handling: Parses the JSON response and extracts entity sentiment details.

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Interpreting the Results

Text:         Barack Hussein Obama
Sentiment:    positive
Entity:       PERSON

Text:         United States
Sentiment:    neutral
Entity:       LOCATION

• The model detects entities (e.g., “Barack Hussein Obama” is classified as a PERSON, “United States” as a LOCATION).
• It assigns a sentiment (positive, negative, or neutral) to each entity based on its context in the text.

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Why Eden AI Is the Best Tool for Entity Sentiment Analysis

Multi-Provider Support

Access top-tier NLP providers like Google, IBM, and Amazon through a single API, streamlining integration of advanced language processing capabilities into your applications.

Easy Integration

The simple API structure ensures quick and easy implementation, allowing you to seamlessly integrate powerful features into your system with minimal effort and setup.

Scalability

Perfect for both small projects and large-scale applications, offering scalable performance to meet diverse needs.

Flexible Pricing

Provides flexible pricing options with free credits to get started and pay-as-you-go plans that allow you to only pay for what you use, ensuring cost efficiency and scalability as your needs evolve.

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Conclusion

In this guide, we explored how to implement Entity Sentiment Analysis using Python and Eden AI’s API.

By following these steps, you can extract valuable insights from text data, helping improve brand monitoring, customer sentiment analysis, and decision-making processes.

Sign up for Eden AI today and integrate Entity Sentiment Analysis into your project!