Mood Gauge API

Mood Gauge API

Mood Gauge API utilizes advanced NLP to discern emotions in text, offering insights into attitudes, opinions, and trends for informed decision-making.

API description

About the API:  

Mood Gauge API is a sophisticated natural language processing (NLP) tool designed to discern and evaluate the emotional tone implicit in textual content. Employing advanced linguistic algorithms and machine learning techniques, this technology enables the automatic extraction of sentiments, emotions and attitudes expressed in written communication. The main goal of a Mood Gauge API is to provide valuable information about the subjective aspects of the text, offering a nuanced understanding of the sentiment conveyed by the author.

In essence, a Mood Gauge API works by analyzing contextual clues, linguistic patterns and semantic structures of a given text. It trains on extensive datasets to recognize and classify sentiment in the text. The system leverages a combination of rule-based algorithms and machine learning models, honed to accurately interpret the complexities of human language.

The sentiment classification phase is where the heart of the Mood Gauge API resides. Using a trained model, the system evaluates the input text and assigns sentiment labels based on patterns learned from the training data. This classification can range from positive sentiments indicating approval or satisfaction to negative sentiments reflecting dissatisfaction or discontent. Neutral sentiments are also recognized in cases where the text lacks a discernible emotional tone.

One of the strengths of Mood Gauge API is their adaptability to various domains and industries. Whether applied to social media monitoring, customer sentiment analysis, brand reputation management or market research, Mood Gauge API provide valuable information that enables decision makers to respond effectively to evolving trends and sentiment. By gauging public opinion and identifying emerging patterns, companies can refine their strategies, improve customer satisfaction and proactively address issues.

In summary, a Mood Gauge API represents a fundamental breakthrough in the field of natural language processing, as it enables the automatic extraction of sentiment from textual content. With applications spanning diverse industries, this technology plays a crucial role in improving decision-making processes, understanding audience sentiment, and revealing valuable information in the vast landscape of human communication.

 

What this API receives and what your API provides (input/output)?

It will receive parameters and provide you with a JSON.

 

What are the most common uses cases of this API?

  1. Social Media Monitoring: Analyzing user comments, tweets, and posts to gauge public sentiment towards a brand, product, or topic.

    Customer Feedback Analysis: Evaluating customer reviews and feedback to understand satisfaction levels and identify areas for improvement.

    Brand Reputation Management: Tracking online mentions and reviews to manage and enhance the reputation of a brand or company.

    Market Research: Assessing market sentiment to understand consumer preferences, emerging trends, and competitor strengths and weaknesses.

    Product Launch Evaluation: Analyzing sentiment before, during, and after a product launch to measure reception and make strategic adjustments.

 

Are there any limitations to your plans?

Besides the number of API calls per month, there are no other limitations. 

API Documentation

Endpoints


To use this endpoint you must indicate a text to obtain a sentiment analysis of the text.



                                                                            
POST https://zylalabs.com/api/3194/mood+gauge+api/3414/sentiment+analysis
                                                                            
                                                                        

Sentiment analysis - Endpoint Features
Object Description
Request Body [Required] Json
Test Endpoint

API EXAMPLE RESPONSE

       
                                                                                                        
                                                                                                                                                                                                                            {"negative":[],"positive":[],"score":0,"scored_words":0,"verdict":"neutral","words":2}
                                                                                                                                                                                                                    
                                                                                                    

Sentiment analysis - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/3194/mood+gauge+api/3414/sentiment+analysis' --header 'Authorization: Bearer YOUR_API_KEY' 

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Mood Gauge API REST API, simply include your bearer token in the Authorization header.

Headers

Header Description
Authorization [Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.


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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.

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 Service Level
100%
 Response Time
3,236ms

Category:

NLP

Tags:


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