Emotion Tone Analyzer API

Emotion Tone Analyzer API

"Emotion Tone Analyzer API analyzes text data to determine sentiment, providing users on customer opinions and attitudes."

API description

About the API:  

The Emotion Tone Analyzer API designed to analyze text data and determine the sentiment expressed in it. Sentiment analysis, also known as opinion mining, is the process of identifying and categorizing the sentiment or emotional tone conveyed in a text as positive, negative or neutral. The Emotion Tone Analyzer API uses natural language processing (NLP) techniques and machine learning algorithms to accurately identify and classify sentiment in text data, providing valuable information about the attitudes, opinions and emotions expressed by users or customers.

In essence, the Emotion Tone Analyzer API works by processing the input text data and applying various linguistic and statistical analysis techniques to determine the sentiment expressed in the text. This is done by analyzing the textual content, identifying keywords and phrases associated with different sentiments, and assigning sentiment labels based on the overall tone and context of the text.

Overall, the Emotion Tone Analyzer API is a valuable tool for users looking to gain actionable insights from text data and understand the sentiment expressed by their users or customers. By accurately analyzing and categorizing sentiment in text data, the API makes it possible to monitor customer sentiment, identify trends and patterns, and make informed decisions to improve customer satisfaction, enhance brand reputation and drive business growth.

 

What this API receives and what your API provides (input/output)?

It will receive parameters and provide you with a JSON.

 

What are the most common uses cases of this API?

  1. Social media monitoring: Analyze user-generated content on social media platforms to understand public opinion, sentiment trends and brand perception.

    Customer feedback analysis: Analyze customer feedback from surveys, reviews and support interactions to identify areas for improvement and measure customer satisfaction.

    Brand reputation management: Monitor online mentions of a brand or product to assess brand sentiment, identify potential reputational risks and take proactive steps to mitigate negative sentiment.

    Market research: Analyze textual data from market research surveys, focus groups and customer interviews to understand consumer attitudes, preferences and sentiment towards products or services.

    Product review analysis: Analyze product reviews and ratings from e-commerce platforms to assess customer sentiment, identify product strengths and weaknesses, and inform product development decisions.

     

Are there any limitations to your plans?

Basic Plan: 300 requests per day.

Pro Plan: 700 requests per day.

Pro Plus Plan: 1,400 requests per day.

Premium Plan: 2,800 requests per day.

Elite Plan: 5,600 requests per day.

Ultimate Plan: 11,200 requests per day.

 

API Documentation

Endpoints


To use this endpoint you must enter a text and the language in which it is written (English = en or German = de) in the parameters.



                                                                            
GET https://zylalabs.com/api/3545/emotion+tone+analyzer+api/3890/sentiment+detection
                                                                            
                                                                        

Sentiment detection - Endpoint Features
Object Description
text [Required] Indicates a text
language [Required] Indiocates a language
Test Endpoint

API EXAMPLE RESPONSE

       
                                                                                                        
                                                                                                                                                                                                                            {"document":{"sentiment":"negative","sentimentWithNeutral":"negative","confidence":0.4515},"sentences":[{"sentiment":"neutral","offset":0,"confidence":0.36,"length":11}]}
                                                                                                                                                                                                                    
                                                                                                    

Sentiment detection - CODE SNIPPETS


curl --location --request GET 'https://zylalabs.com/api/3545/emotion+tone+analyzer+api/3890/sentiment+detection?text=Im very sad&language=en' --header 'Authorization: Bearer YOUR_API_KEY' 

    

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 Emotion Tone Analyzer API REST 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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To use this API, users must enter a text to obtain a sentiment analysis.

Emotion Tone Analyzer API designed to analyze text data and detect the emotional tone expressed in it.

There are different plans to suit all tastes, including a free trial for a small number of requests, but your rate is limited to avoid abuse of the service.

Zyla provides a wide range of integration methods for almost all programming languages. You can use these codes to integrate with your project as you need.

Sure, you can use it and pay only when you use it. The credit can’t be expired.

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Prices are based on a recurring monthly subscription depending on the plan selected — plus overage fees applied when a developer exceeds a plan’s quota limits. In this example, you'll see the base plan amount as well as a quota limit of API requests. Be sure to notice the overage fee because you will be charged for each additional request.

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 Service Level
75%
 Response Time
278ms

Category:

NLP

Tags:


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