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/10932/extract+tone+from+text+using+api/20642/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. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The Tone Checker endpoint returns a structured analysis of the emotional tone of the input text. This includes a tone distribution with detected emotions, their respective percentages, an interpretive analysis of the text, and practical suggestions for improving communication.
The key fields in the response data include `toneDistribution`, which lists detected tones and their percentages, `analysis`, providing a summary of the emotional content, and `suggestions`, offering advice on enhancing communication effectiveness.
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`, making it easy to parse and utilize.
The Tone Checker provides information on emotional tones detected in the text, their percentages, a detailed interpretive analysis of the emotional message, and actionable suggestions for improving the tone of communication.
Users can customize their requests by varying the input text sent to the Tone Checker endpoint. Different texts will yield different tone analyses, allowing users to assess various messages and their emotional impacts.
Typical use cases include analyzing customer feedback for sentiment, enhancing corporate communication, evaluating social media posts, and studying literary texts to understand emotional nuances and improve engagement.
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 understanding of emotional tones.
Standard data patterns include a clear distribution of tones, often with one or two predominant emotions. Users can expect to see percentages that reflect the emotional weight of the text, along with insightful analysis and tailored suggestions for improvement.
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