Retrieve AI Tone Content API API ID: 10933

Effortlessly analyze and retrieve the tone of your text content with our AI-powered API, ensuring effective communication.
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This API is designed to analyze the emotional tone of any text, providing detailed insight into the affective charge and mood conveyed by the content. Its operation is simple and efficient: the user sends a text as input, and the API responds with a structured analysis that includes the different tones detected, the percentage of each one's presence, and an interpretive summary.

For example, it can identify emotions such as positivity, neutrality, frustration, or sadness, indicating the proportion of each within the text. In addition, it offers a descriptive analysis section where it interprets the result, explaining in an understandable way what type of emotional message predominates, how it develops throughout the text, and what factors influence the overall tone. Complementing this information, it also generates practical suggestions for adjusting or improving communication: for example, how to emphasize a positive tone, balance emotions, or reduce negative perceptions.

The API is especially useful for those who want to incorporate emotional intelligence into their products or workflows. It can be applied to social media analysis, customer feedback evaluation, corporate content review, or even the study of literary texts. Its percentage- and category-based approach facilitates automated emotion interpretation, while its textual output provides a humanized and useful context for decision-making.

In short, this API not only measures emotions, but also interprets and suggests communication improvements. It is a powerful tool for understanding how messages are perceived and how they can be optimized to generate greater emotional impact. Thanks to its analytical capabilities, it transforms text into actionable data, helping to make communication a more empathetic, clear, and effective experience.

API Documentation

Endpoints


Analyzes texts with artificial intelligence, detects emotions and predominant tones, displaying percentages, detailed interpretation, and suggestions for improving communication.



                                                                            
GET https://docs.zylalabs.com/api/10933/retrieve+ai+tone+content+api/20643/tone+checker
                                                                            
                                                                        

Tone Checker - Endpoint Features

Object Description
content [Required]

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

content

API EXAMPLE RESPONSE

{"success":true,"content":{"toneDistribution":[{"tone":"\ud83d\ude0a Positive","percentage":80},{"tone":"\ud83d\ude10 Neutral","percentage":10},{"tone":"\ud83d\ude24 Frustrated","percentage":0},{"tone":"\ud83d\ude22 Sad","percentage":10}],"analysis":"The text 'Today I feel very happy.' has a predominantly positive tone indicated by the expression of happiness. The use of the word 'happy' explicitly conveys a sense of joy and positivity. This text contains no elements of frustration or sadness, which is why those tones have a percentage of 0. There is a small neutral component that represents the straightforwardness of the statement, devoid of additional emotional layers.","suggestions":"To maintain the positive tone, consider adding more context or reasons for your happiness that could enhance the emotional impact. If aiming for variety in tone, include additional sentences that describe different feelings or experiences throughout the day, balancing positive expressions with neutral observations or subtle challenges overcome."}}

Tone Checker - CODE SNIPPETS


curl --location --request GET 'https://zylalabs.com/api/10933/retrieve+ai+tone+content+api/20643/tone+checker?content=Today I feel very happy.' --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 Retrieve AI Tone Content 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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Retrieve AI Tone Content API FAQs

The Tone Checker endpoint returns a structured analysis of the emotional tone of the input text. This includes a tone distribution with percentages for each detected tone, a detailed interpretive analysis, and practical suggestions for improving communication.

The key fields in the response data include "toneDistribution" (an array of detected tones and their percentages), "analysis" (a summary of the emotional tone), and "suggestions" (recommendations for enhancing communication).

The response data is organized in a JSON format. It contains a "success" boolean indicating the request status, followed by a "content" object that includes "toneDistribution," "analysis," and "suggestions" for clarity and ease of use.

The Tone Checker provides information on the emotional tones present in the text, their respective percentages, a detailed analysis of the predominant tone, and actionable suggestions for improving the emotional impact of the communication.

Users can customize their data requests by providing different text inputs to the Tone Checker endpoint. The API analyzes the specific text submitted, allowing for tailored emotional tone analysis based on the content provided.

Typical use cases include analyzing customer feedback for sentiment, reviewing corporate communications for emotional impact, evaluating social media posts, and studying literary texts to understand emotional nuances and improve engagement.

Data accuracy is maintained through advanced AI algorithms that analyze text for emotional tone. Continuous training and updates to the model ensure that it adapts to language nuances and improves its understanding of emotional context over time.

Users can utilize the returned data by interpreting the tone distribution to gauge emotional responses, applying the analysis to enhance communication strategies, and implementing the suggestions to adjust messaging for better emotional resonance with their audience.

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