The API allows you to analyze and understand the sentiment of text data, helping you determine if a phrase or expression is neutral, positive, or negative.

The API has one endpoint, "SENTIMENT ANALYZER," which performs the sentiment analysis on the provided text.

The JSON response will contain the following information: "sentiments_detected": An array of detected sentiments with corresponding scores for negativity (neg), neutrality (neu), positivity (pos), and an overall compound score. "sentiment": The overall sentiment label, which in this case will be "positive." "success": A boolean value indicating the success of the sentiment analysis.

The "compound" score is an overall sentiment score that combines the positive and negative sentiment scores.

The API currently supports analyzing one sentence or phrase at a time. If you have multiple sentences, you would need to make separate API calls for each one.

The Sentiment Analyzer endpoint returns a JSON object containing the sentiment classification of the input text, along with a confidence score indicating the strength of the sentiment detected.

The key fields in the response include "sentiment," which indicates the overall sentiment (positive, negative, or neutral), and "score," which provides a confidence level for the sentiment classification.

Users can customize their requests by providing different text inputs for analysis. The API does not require additional parameters for sentiment analysis, making it straightforward to use.

Typical use cases include analyzing customer feedback, monitoring social media sentiment, assessing brand reputation, and evaluating news coverage to understand public opinion.

The response data is organized in a JSON format, with clear key-value pairs that indicate the sentiment type and its corresponding confidence score, making it easy to parse and utilize.

The Text Summarizer endpoint provides a condensed version of the input text based on a specified percentage, allowing users to quickly grasp the main ideas without reading the entire content.

Users can leverage the sentiment and score to gauge customer opinions, inform marketing strategies, and identify areas for improvement based on the emotional tone of the analyzed text.

The API employs advanced natural language processing techniques and machine learning models that are regularly updated and trained on diverse datasets to ensure high accuracy in sentiment classification.

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