To use this API, users must enter a text to obtain a sentiment analysis.

The Mood Gauge API is a specialized service that utilizes advanced natural language processing (NLP) algorithms to assess and classify the emotional tone or mood expressed in textual content. It provides a systematic way to gauge sentiments and emotions within written communication.

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.

There are different plans suits everyone including a free trial for small amount of requests, but it’s rate is limit to prevent abuse of the service.

The Mood Gauge API returns a JSON object containing sentiment analysis results, including classifications of sentiments (positive, negative, neutral), a sentiment score, and details about the words analyzed.

Key fields in the response include "negative" and "positive" arrays for identified sentiments, "score" indicating overall sentiment strength, "verdict" for sentiment classification, and "words" showing the number of words analyzed.

The primary parameter for the Mood Gauge API is the "text" input, which should contain the textual content you want to analyze for sentiment.

The response data is structured as a JSON object with arrays for positive and negative sentiments, a numerical score, a verdict indicating the overall sentiment, and a count of analyzed words, making it easy to interpret.

Typical use cases include analyzing social media comments for brand sentiment, evaluating customer feedback for service improvement, and monitoring market trends to inform strategic decisions.

Data accuracy is maintained through extensive training on diverse datasets, employing advanced NLP algorithms and machine learning techniques to ensure reliable sentiment classification.

In the response, "negative" and "positive" arrays list words associated with each sentiment, "score" quantifies sentiment strength, "verdict" summarizes the overall sentiment, and "words" indicates the total number of words processed.

Users can leverage the returned data to gauge public sentiment, identify trends, and make informed decisions by analyzing the sentiment score and specific word associations in their text inputs.

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