The Sentiment Detection API returns a JSON structure containing the analyzed text, the detected sentiment (either "POSITIVE" or "NEGATIVE"), and a numerical emotional intensity score ranging from -1 to 1.

The key fields in the response data include "text" (the input text), "sentiment" (the sentiment classification), and "score" (the numerical intensity of the sentiment).

The response data is organized in a JSON format with three main fields: "text" for the original input, "sentiment" for the classification, and "score" for the emotional intensity, allowing for easy parsing and integration.

The primary parameter for the Sentiment Detection API is "text," which must be provided as input. Users can customize their requests by varying the text content they analyze.

Typical use cases include analyzing product reviews, monitoring social media sentiment, enhancing customer service interactions, conducting surveys, and evaluating user-generated content for emotional insights.

Data accuracy is maintained through continuous model training and validation against diverse datasets, ensuring the sentiment analysis reflects real-world language use and emotional expression.

Users can utilize the returned data by integrating the sentiment and score into applications for real-time feedback, trend analysis, or automated responses in customer service and marketing strategies.

Standard data patterns include a clear classification of sentiment and a corresponding score that indicates emotional intensity, such as "NEGATIVE" with a score of -0.556 for negative sentiments.

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