The Emotion Tone Content Fetcher API is a tool based on natural language processing (NLP) that allows you to analyze written texts and obtain a detailed assessment of their emotional content. This API receives simple text as input and returns a JSON structure with positive, negative, and neutral sentiment scores, along with an overall score that summarizes the general emotional orientation of the text.
Thanks to semantic analysis models trained with large volumes of linguistic data, this API can accurately identify the emotional tone of phrases in natural language. For example, when analyzing a phrase such as “I love it,” the system returns a high positive score (0.677), moderate neutral score (0.323), zero negative score, and an overall score of 0.6369, indicating a strongly positive emotional expression.
This API is especially useful in multiple scenarios: from social media monitoring, product or service review analysis, customer feedback systems, to human resources applications that seek to assess the emotional climate in internal surveys. It can also be easily integrated into analytical dashboards, chatbots, or business intelligence tools.
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.
Sentiment detection - Endpoint Features
| Object | Description |
|---|---|
text |
[Required] Indicates a text |
{"document":{"sentiment":"negative","sentimentWithNeutral":"negative","confidence":0.4515},"sentences":[{"sentiment":"neutral","offset":0,"confidence":0.36,"length":11}]}
curl --location --request GET 'https://zylalabs.com/api/7821/emotion+tone+content+fetcher+api/12814/sentiment+detection?text=i love it' --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.
To use this API, users must enter a text to obtain a sentiment analysis.
Emotion Tone Content Fetcher API designed to analyze text data and detect the emotional tone expressed in it.
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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.
The API returns a JSON object containing sentiment analysis results, including overall sentiment, confidence scores, and detailed sentiment for individual sentences.
Key fields include "sentiment" (overall sentiment), "confidence" (certainty of the sentiment), and "sentences" (array detailing sentiment for each sentence with offsets and lengths).
The response is structured as a JSON object with a "document" section for overall sentiment and a "sentences" array for detailed analysis of each sentence.
The endpoint accepts parameters such as "text" (the input text for analysis) and "language" (to specify the language, e.g., 'en' for English or 'de' for German).
The endpoint provides insights into the emotional tone of the text, including sentiment classification (positive, negative, neutral) and confidence levels for both overall and sentence-specific sentiments.
Users can analyze the overall sentiment to gauge customer opinions and use sentence-level data to identify specific areas of concern or positive feedback, aiding in targeted responses.
The API employs natural language processing and machine learning algorithms, continuously trained on diverse datasets to enhance accuracy in sentiment detection.
Common use cases include monitoring social media sentiment, analyzing customer feedback, and assessing brand reputation, helping businesses make informed decisions based on emotional insights.
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