Text Emotion Retrieval API

API ID 10878

Discover and retrieve emotions embedded in your text using the Text Emotion Retrieval API for deeper insights.

100% uptime 2,398 ms avg response

API Documentation

Endpoints

Request

It analyzes a text and returns levels of emotions such as happiness, anger, sadness, surprise, and fear, indicating the intensity of each one.

Endpoint ID: 20583
POST https://pr213-testing.zylalabs.com/api/10878/text+emotion+retrieval+api/20583/emotion+detection
INPUT PARAMETERS

Emotion Detection — Endpoint Features

Object Description
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

API EXAMPLE RESPONSE
JSON
{
    "Happy": 1.0,
    "Angry": 0.0,
    "Surprise": 0.0,
    "Sad": 0.0,
    "Fear": 0.0
}
Emotion Detection — CODE SNIPPETS

curl --location --request POST 'https://pr213-testing.zylalabs.com/api/10878/text+emotion+retrieval+api/20583/emotion+detection' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw 'I am very happy to use this API.'

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Text Emotion Retrieval API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Overview

The Text Emotion Retrieval API allows you to identify and quantify the emotions present in any piece of text. Its goal is to help understand the emotional tone behind written language, offering valuable information to optimize human interactions, improve communication, and enrich content analysis.

Upon receiving a text, the API analyzes its linguistic content and returns a set of basic emotions, such as happiness, anger, sadness, surprise, and fear, each accompanied by a numerical value indicating its intensity or level of presence. These values, expressed as scores from 0 to 1, allow you to accurately determine the predominant emotion in the text and the extent to which the others are manifested.

This service is particularly useful for applications in the fields of sentiment analysis, customer service, marketing, social research, social media monitoring, chatbot development, and review analysis. Companies can use it to assess the emotional state of their customers, measure the reaction to advertising campaigns, or adjust automated communication according to the detected tone.

In short, the Text Emotion Retrieval API is a powerful and flexible solution for transforming text into meaningful emotional information. With it, developers and analysts can better understand the emotions behind words and make more informed decisions based on the emotional state of users or audiences.

Text Emotion Retrieval API FAQs

The Emotion Detection endpoint returns a JSON object containing scores for five basic emotions: happiness, anger, sadness, surprise, and fear. Each emotion is represented by a numerical value ranging from 0 to 1, indicating its intensity in the analyzed text.

The key fields in the response data are "Happy," "Angry," "Sad," "Surprise," and "Fear." Each field corresponds to an emotion and contains a score that reflects the intensity of that emotion in the provided text.

The response data is organized as a JSON object with emotion labels as keys and their corresponding intensity scores as values. This structure allows for easy parsing and interpretation of the emotional content of the text.

Typical use cases include sentiment analysis for customer feedback, enhancing chatbot interactions based on user emotions, monitoring social media sentiment, and analyzing emotional responses to marketing campaigns or content.

Users can customize their data requests by providing different text inputs to the Emotion Detection endpoint. The API analyzes the specific text submitted, allowing for tailored emotional insights based on varying content.

Data accuracy is maintained through advanced natural language processing algorithms that analyze linguistic patterns and context. Continuous updates and improvements to the model ensure that it adapts to evolving language use and emotional expression.

Standard data patterns include varying intensity scores for emotions based on the text's tone. For instance, a text expressing joy may yield a high happiness score and low scores for other emotions, while a text with conflict may show higher anger and fear scores.

Users can utilize the returned data by interpreting the scores to gauge the emotional tone of the text. For example, a high happiness score can indicate positive sentiment, guiding responses in customer service or content creation strategies.

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