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The Ideal Text Sentiment Analysis API For Software Developers

Business owners can improve their commercial performance and optimize their business experience when they learn about their customers opinion.

It would be impossible to scan all the content in social media to find out percentages of opinion, and this is when they request from developers to tailor software that can do it automatically.

The Ideal Text Sentiment Analysis API For Software Developers

Sentiment analysis is a subfield of NLP and computational linguistics that focuses on the identification and analysis of subjective emotional states in texts. It also includes opinion mining and sentiment analysis in general. The goal of sentiment analysis is to determine the emotional state of the author or narrator of a feedback text, whether it is a book, an article, or a tweet. To devise tools for their clients, developers need an efficient application like Opinion Analysis API.

This sentiment analysis API is a user-friendly platform that allows to quickly and easily analyze the sentiments found in texts. This means that the user can easily determine whether a given text is positive, negative, or neutral. Manual sentiment analysis is time-consuming and expensive. This is where a text analytics API comes onto the stage.

Text analysis and opinion mining API is extremely simple to use. By just providing the text to analyze, the rest is performed by the software. This tool can handle large amounts of text, so users don’t have to worry about publishing their data. This API supports multiple languages, including English, Spanish, French, German and Portuguese. This means that one can use it for international projects without no restrictions.

The most common use cases for sentiment analysis are:
– To monitor and analyze trends in public opinion;
– To understand why people interact with one´s brand or product;
– To understand how people feel about one´s product or service.

Additionally, this API allows to extract opinions from texts by simply providing the texts one needs to analyze. These opinions can then be used to better understand one´s customers’ needs and desires so as to improve one´s products and services as well as one´s marketing strategy. It´s also useful to scan opinion about one´s competitors´ brand, to inspire on approaches to foster success, or abandon unsuccessful techniques.

Another use of sentiment analysis is to gauge public opinion on current events and issues. This can be helpful for politicians who want to know what voters think about their policies.
Sentiment analysis is also useful for marketing purposes; it can help companies understand how customers feel about their products and services. This can help them improve their offerings and better serve their customers.

This API also supports JSON format outputs, which means that it’s really easy to integrate it into any projects. Simply provide the text to analyze as a URL to get the results in no time! To make a long story short, sentiment analysis is a method of understanding the emotional state of a piece of text. This can be done by identifying the presence of certain words or phrases.

How To Get Started With This Text Analytics API

The Ideal Text Sentiment Analysis API For Software Developers

Once you count on a subscription on Zyla API Hub marketplace, just start using, connecting and managing APIs. Subscribe to Opinion Analysis API by simply clicking on the button “Start Trial”. Then meet the needed endpoint and simply provide the search reference. Make the API call by pressing the button “test endpoint” and see the results on display. The AI will process and retrieve an accurate report using this data.

Opinion Analysis API examines the input and processes the request using the resources available (AI and ML). In no time at all the application will retrieve an accurate response. The API has one endpoint to access the information: Analyzer, where you insert the opinión you need to analyze.

If the input is “id”: 1, “language”: “en”, “text”: “Guess it´s ok” in the endpoint, the response will look like this:

{ "id": "4", "predictions": [ { "probability": 1, "prediction": "Indifferent" }
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