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summary

Entity Sentiment Analysis combines Named Entity Recognition with sentiment analysis to determine the sentiment (positive, negative, or neutral) associated with specific entities in text.
Developers can Do Entity Sentiment Analysis Using Python using a REST API that accepts standard inputs and returns structured JSON responses.
Eden AI's Entity Sentiment Analysis API makes it easy to analyze how text feels about specific entities.
Developers can Do Entity Sentiment Analysis Using Python using a REST API that accepts standard inputs and returns structured JSON responses.
The integration works with Python, JavaScript, PHP, and any HTTP-capable language with minimal setup.

In this tutorial, you will learn how to use the Entity Sentiment Analysis API in 5 minutes using Python. Eden AI provides an easy and developer-friendly API that allows you to analyze sentiment at the entity level in text.

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What is Entity Sentiment Analysis?

Entity Sentiment Analysis combines Named Entity Recognition with sentiment analysis to determine the sentiment (positive, negative, or neutral) associated with specific entities in text.

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Getting Started

1. Sign Up: Create a free account here.

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How to Implement Entity Sentiment Analysis using Python



import requests

headers = {"Authorization": "Bearer YOUR_API_TOKEN"}

url = "https://api.edenai.run/v2/text/entity_sentiment"
json_payload = {
    "providers": "google",
    "language": "en",
    "text": "Apple is doing great, but Microsoft had issues.",
}

response = requests.post(url, json=json_payload, headers=headers)
print(response.json())

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Conclusion

Eden AI's Entity Sentiment Analysis API makes it easy to analyze how text feels about specific entities. Sign up free and start today!

FAQ — Do Entity Sentiment Analysis Using Python

You need an API key from your chosen AI provider. Eden AI lets you access multiple providers with a single key, removing the need for separate vendor accounts.

Any language that supports HTTP requests works — Python, JavaScript, PHP, Ruby, Go, and more. Ready-to-use code snippets are available for the most common languages.

Most developers complete a basic integration in under an hour using standardized API endpoints and ready-to-use code examples.

Implement exponential backoff for rate limit errors and use try-catch blocks for network failures. Eden AI's built-in fallback routing automatically redirects requests if a provider is unavailable.

Eden AI supports GDPR-compliant provider filtering and does not store or reuse your data, ensuring compliance with European privacy regulations.

Last updated onMay 22, 2026

Taha Zemmouri

Taha Zemmouri is the CEO and co-founder of Eden AI. With previous experience in AI consulting, he brings a strong business perspective to artificial intelligence and focuses on turning AI capabilities into practical value for companies. With a background in data science and a real entrepreneurial mindset, he combines technical understanding, business vision, and hands-on execution to make AI more accessible and easier to integrate.

How to Do Entity Sentiment Analysis Using Python

What is Entity Sentiment Analysis?

Getting Started

How to Implement Entity Sentiment Analysis using Python

Conclusion

FAQ — Do Entity Sentiment Analysis Using Python

What do I need to Do Entity Sentiment Analysis Using Python?

Which programming languages can I use?

How long does the integration take?

How do I handle errors and rate limits?

Is the data I send secure and GDPR-compliant?

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