In the field of natural language processing, understanding the underlying sentiment in textual content is paramount for applications ranging from customer feedback analysis to social media monitoring. The Emotional Analysis Engine API emerges as a robust tool, which leverages advanced machine learning algorithms to analyze and decipher the emotional tone expressed in sentences. This API enables users to gain valuable insights into user sentiments, improving decision-making processes and user experiences.
The Emotional Analysis Engine API is based on state-of-the-art sentiment analysis algorithms that provide accurate assessments of the emotional tone expressed in sentences. It goes beyond simple polarity analysis, capturing nuances to distinguish between positive, negative and neutral sentiments.
Operating in real time, the API provides immediate feedback on the sentiments expressed in sentences. This is especially valuable for applications such as social media monitoring, customer service interactions and sentiment analysis at live events.
The Emotional Analysis Engine API is emerging as an essential tool for extracting valuable information from textual content. Whether it's understanding customer sentiment, tracking public opinion or improving chatbot interactions, this API equips developers and enterprises with advanced sentiment analysis capabilities. In an era where understanding the emotional tone of text is crucial, the Emotional Analysis Engine API stands as a reliable and indispensable asset for those looking to unlock deeper insights from their textual data.
It will receive parameters and provide you with a JSON.
Social media monitoring: Analyze sentiment on social media posts and comments to understand public opinion, track trends and manage brand reputation effectively.
Customer feedback analysis: Evaluate sentiments in customer reviews, surveys and comments to gain insights into customer satisfaction and improve products or services.
Chatbot enhancements: Improve chatbot interactions by incorporating sentiment analysis, enabling more empathetic and context-aware responses to user queries.
Brand reputation management: Monitor sentiment associated with a brand in mentions, reviews and online discussions to proactively manage and improve brand reputation.
Market research: Use sentiment analysis in market research surveys, focus groups and interviews to accurately understand consumer preferences and trends.
Basic Plan: 6,000 API Calls and 200 requests per day.
Pro Plan: 12,000 API Calls and 400 requests per day.
Pro Plus Plan: 24,000 API Calls and 800 requests per day.
To use this endpoint you must enter a text in the parameter.
Get Emotions - Endpoint Features
Object | Description |
---|---|
Request Body |
[Required] Json |
{"compound":0.6808225270250103,"negative":0,"neutral":0.5172413793103449,"positive":0.48275862068965514}
curl --location --request POST 'https://zylalabs.com/api/2925/emotional+analysis+engine+api/3056/get+emotions' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"sentence": "I am excited to go to the festival"
}'
Header | Description |
---|---|
Authorization
|
[Required] Should be Bearer access_key . See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
To use this API the user must indicate a text to analyze and obtain the emotions of the text.
There are different plans suits everyone including a free trial for small amount of requests, but itβs rate is limit to prevent abuse of the service.
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.
The Emotional Analysis Engine API is an advanced natural language processing (NLP) tool designed to analyze and determine the emotional tone or sentiment expressed in textual content.
The API returns a JSON object containing sentiment analysis results, including values for compound, negative, neutral, and positive sentiments based on the analyzed text.
The key fields in the response include "compound" (overall sentiment score), "negative" (negative sentiment score), "neutral" (neutral sentiment score), and "positive" (positive sentiment score).
The response data is structured as a JSON object with key-value pairs, allowing easy access to sentiment scores. For example, a typical response might look like: {"compound":0.68, "negative":0, "neutral":0.52, "positive":0.48}.
The primary parameter for the endpoint is the text input, which users must provide for sentiment analysis. Additional parameters may include language settings, depending on the API's capabilities.
Users can leverage the sentiment scores to gauge emotional tone, inform marketing strategies, enhance customer service interactions, or improve user engagement by tailoring responses based on sentiment analysis.
Typical use cases include monitoring social media sentiment, analyzing customer feedback, enhancing chatbot interactions, and conducting market research to understand consumer preferences.
Data accuracy is maintained through advanced machine learning algorithms that continuously learn from diverse datasets, ensuring reliable sentiment analysis across various contexts and languages.
Users can expect a range of sentiment scores from -1 (very negative) to +1 (very positive) in the compound field, with corresponding values in negative, neutral, and positive fields reflecting the emotional tone of the input text.
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