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.
Analyzes texts with artificial intelligence, detects emotions and predominant tones, displaying percentages, detailed interpretation, and suggestions for improving communication.
Tone Checker - Endpoint Features
| Object | Description |
|---|---|
content |
[Required] |
{"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."}}
curl --location --request GET 'https://zylalabs.com/api/10931/text+emotion+capture+api/20639/tone+checker?content=Today I feel very happy.' --header 'Authorization: Bearer YOUR_API_KEY'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
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The Tone Checker endpoint returns a structured analysis of the emotional tones detected in the input text. This includes a tone distribution with percentages for each identified emotion, a detailed interpretive analysis, and practical suggestions for improving communication.
The key fields in the response data include "toneDistribution" (which lists detected tones and their percentages), "analysis" (a summary of the emotional tone), and "suggestions" (advice for enhancing communication).
The response data is organized in a JSON format. It contains a "success" field indicating the request status, followed by a "content" object that includes tone distribution, analysis, and suggestions for improvement.
The Tone Checker provides information on emotional tones present in the text, their respective percentages, a detailed analysis of the emotional message, and actionable suggestions for enhancing the text's emotional impact.
Users can customize their requests by varying the input text they send to the Tone Checker endpoint. Different texts will yield different tone analyses and suggestions based on the emotional content present.
Typical use cases include analyzing social media posts for emotional engagement, evaluating customer feedback to understand sentiment, reviewing corporate communications for tone consistency, and studying literary texts for emotional depth.
Data accuracy is maintained through advanced artificial intelligence algorithms that analyze text patterns and emotional cues. Continuous updates and training on diverse datasets help improve the model's performance and reliability.
Users can utilize the returned data by interpreting the tone distribution to gauge emotional impact, applying the analysis to refine their messaging, and implementing the suggestions to enhance reader engagement and emotional resonance.
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