The Analyzer endpoint returns sentiment analysis results for the provided text, including a sentiment label (positive, negative, or neutral) and a confidence score indicating the reliability of the prediction.

The key fields in the response data include "text" (the analyzed input), "label" (the sentiment classification), and "confidence" (a score between 0 and 1 representing the certainty of the sentiment prediction).

The response data is structured as a JSON object containing a "results" array. Each entry in the array includes the analyzed text, its sentiment label, and the corresponding confidence score.

The primary parameter for the Analyzer endpoint is the "text" parameter, which accepts any string input in supported languages for sentiment analysis.

Data accuracy is maintained through continuous training of the underlying AI model on diverse datasets, ensuring it effectively understands sentiment nuances across different languages and contexts.

Typical use cases include analyzing customer feedback on products, monitoring social media sentiment, and assessing customer satisfaction in support interactions to inform business decisions.

Users can leverage the sentiment label to gauge public opinion and the confidence score to prioritize responses or actions based on the reliability of the sentiment analysis.

The Analyzer endpoint provides insights into the emotional tone of text, helping users understand customer sentiments, identify trends, and make data-driven decisions based on public perception.

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