NEW: Entity Sentiment Analysis available on Eden AI
Tags: ai, api, entitysetiment, targetedsentiment
Published: 2023-09-15 08:07:07

_Quickly extract and analyze the sentiment associated with specific entities in your text data with just a few simple steps!_

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## **What is [Entity Sentiment Analysis API](https://www.edenai.co/feature/entity-sentiment-analysis?referral=new-feature-entity-sentiment)?**
Entity Sentiment Analysis (or [Targeted Sentiment Analysis](https://www.edenai.co/feature/entity-sentiment-analysis?referral=new-feature-entity-sentiment)) is an advanced form of Traditional Sentiment Analysis that goes beyond analyzing the overall sentiment of a piece of text. Instead, it focuses on identifying and analyzing the sentiment associated with specific entities mentioned in the text, such as people, products, organizations, or topics.

Entity Sentiment Analysis uses both NLP and Sentiment Analysis to identify the sentiment (positive or negative) conveyed about entities in the text, offering a more detailed and finely-tuned comprehension of the sentiments embedded within the text.

After detecting the entities referenced in a document and which portions of the document discuss each item, it reliably predicts the attitude conveyed about each unique entity in the text, even when the mood about each is different.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/uluvw712o3hiv016nbp0.png)



While Traditional Sentiment Analysis provides an overall sentiment score for a piece of text, Targeted Sentiment Analysis delves deeper by analyzing sentiment associated with specific entities within the text. Choosing between the two techniques depends on your project goals and the level of detail you require to make informed decisions or gain valuable insights from the text data.

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**[Try Eden AI for FREE](https://app.edenai.run/user/register?referral=new-feature-entity-sentiment)**

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## **Access many Entity Sentiment Analysis Providers with one API**
Our standardized API allows you to use different providers on Eden AI to easily integrate Entity Sentiment Analysis APIs into your system.

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**Amazon Targeted Sentiment - Available on [Eden AI](https://app.edenai.run/user/register?referral=new-feature-entity-sentiment)**
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![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/apncjyxt3xcy272dtax8.png)




Amazon Comprehend is a natural-language processing (NLP) service that employs machine learning to analyze text data for insights. Targeted Sentiment is a new API from Comprehend that gives more detailed sentiment insights by recognizing the sentiment (positive, negative, neutral, or mixed) towards entities within the text.

The API can identify various entity types, including PERSON, LOCATION, ORGANIZATION, FACILITY, BRAND, COMMERCIAL_ITEM, MOVIE, MUSIC, BOOK, SOFTWARE, GAME, PERSONAL_TITLE, EVENT, DATE, QUANTITY, etc., making it versatile for analyzing different types of content.

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**Google - Available on [Eden AI](https://app.edenai.run/user/register?referral=new-feature-entity-sentiment)**
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![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/irr23g832h5vebq2qgup.png)

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The Google Cloud Natural Language API performs natural language processing (NLP) to evaluate and extract sentiment from text data. The results can show if a mention of the entity is favorable, negative, or neutral. Additionally, this API can locate things in text (such as persons, places, and organizations) and provide information about their emotions.

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**[Try these APIs on Eden AI](https://app.edenai.run/user/register?referral=new-feature-entity-sentiment)**


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## **Benefits of using an Entity Sentiment API**
Using an Entity Sentiment API offers a range of benefits that enhance various aspects of text data processing and analysis. Some of the key advantages include:

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**1. Entity-Level Insights:** You can gain insights into how specific entities are perceived by your users. This is especially valuable when you want to assess the sentiment associated with particular products, services, or attributes mentioned in user reviews, comments, or feedback.
**2. Personalization:** Leveraging entity sentiment analysis, your app can personalize user experiences. For example, if a user expresses positive sentiment toward a certain product, your app can recommend similar products or tailor content to their preferences.
**3. Enhanced Customer Support:** By analyzing the sentiment of user queries or support requests, your app can prioritize and route them accordingly. Users with negative sentiment can receive faster or more specialized support, improving their satisfaction.
**4. Save time and resources:** Entity Sentiment APIs can quickly analyze large volumes of text and provide sentiment scores for individual entities (such as people, places, or products) within the text. This can save significant time and resources compared to manual sentiment analysis.
**5. Accuracy:** These APIs are typically trained on vast datasets and use advanced machine learning algorithms to determine sentiment. As a result, they can provide accurate sentiment scores, reducing the likelihood of human bias or error.
**6. Competitive Analysis:** Compare sentiment associated with your entities to that of competitors. This competitive analysis can inform your strategies, helping you understand how you stack up in terms of user perception.
**7. Real-time analysis:** Use Entity Sentiment API to monitor customer feedback in real-time on social media platforms, allowing businesses to respond quickly to customer concerns and enhance customer satisfaction.
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## **What are the uses of Entity Sentiment APIs?**
Entity Sentiment APIs have a wide range of uses across various industries and applications. Here are some common use cases: ‍

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**‍1. Social Media Monitoring**
The sentiment surrounding certain people, companies, or goods referenced in social media postings, comments, and reviews may be tracked and analyzed using entity sentiment APIs. This aids businesses and people in determining how the general audience feels and perceives their products.

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**2. Customer Feedback Analysis**
These APIs may be used by businesses to evaluate customer feedback and reviews to learn how customers feel about their goods and services. This knowledge may direct efforts to enhance products, develop marketing plans, and provide customer service.

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**3. News and Media Analysis**
Entity Sentiment APIs may be used by media companies and news organizations to determine how the public feels about various entities that are referenced in news pieces, enabling more data-driven reporting and analysis.

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**4. Political and Public Opinion Analysis**
Entity Sentiment APIs may be used by academics and political analysts to examine how the general public feels about certain politicians, policies, and topics. This can help in polling the public and determining voter sentiment.

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**5. Content Recommendations**
Entity sentiment analysis may be used by media streaming services and content providers to suggest material to consumers based on their sentiment preferences, enhancing user engagement and happiness.

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## **How to use Entity Sentiment with the Eden AI API?**
To start using Entity Sentiment you need to [create an account on Eden AI for free.](https://app.edenai.run/user/register?referral=new-feature-entity-sentiment) Then, you'll be able to get your API key directly from the homepage and use it with free credits offered by Eden AI.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qhwpwqo3u2dvd97yi9ko.png)



**[Get your APi key for FREE](https://app.edenai.run/user/register?referral=new-feature-entity-sentiment)**
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## **Best Practices for Entity Sentiment on Eden AI**
When implementing Entity Sentiment on Eden AI or any other platform, it's essential to follow certain best practices to ensure optimal performance, accuracy, and security. Here are some general best practices for Entity Sentiment on Eden AI:

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**1. Contextual Analysis:** Consider the context in which entities appear. Sentiment can vary based on the surrounding text, so it's essential to analyze entities in their context. For example, "Apple" can refer to both the company and the fruit, each with different sentiments.
**2. Multilingual Support:** If your application involves multiple languages, ensure that the API supports multilingual sentiment analysis. Different languages may have unique sentiment expressions and nuances.
**3. Handle Ambiguity:** Be aware that entity sentiment analysis can be challenging when entities have ambiguous sentiments. For instance, a review might mention a restaurant's excellent food but criticize its service. Consider how to handle such cases.
**4. Sentiment Scale:** Understand the sentiment scale used by the API. Some systems use a binary positive/negative sentiment scale, while others may use a multi-class scale, such as positive, negative, neutral, or a numerical scale. Ensure that the scale aligns with your application needs.
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**How Eden AI can help you?**
Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/bo7r4covcsl6crahqo5q.gif)


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- Centralized and fully monitored billing on Eden AI for all Entity Sentiment APIs
- Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider
- Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
- The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines)
- Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.
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**[Create your Account on Eden AI](https://app.edenai.run/user/register?referral=new-feature-entity-sentiment)**