Sentiment analysis is the process of determining whether a particular piece of data (a tweet, a comment, a review, etc.) is positive, negative, or neutral. It´s one of the most popular applications of AI and ML.
Developers make use of these applications to devise tools for their clients whose commercial success depends on the customers´ sentiment and opinion.
Sentiment analysis -also referred to as opinion mining or emotional analysis- is a field of study that uses NLP and ML techniques and algorithms to identify and extract subjective information from texts. Sentiment analysis is used in a variety of fields, including marketing, politics, journalism, and public relations. It is also used by businesses to better understand their customers and by governments to monitor public opinion. The strongest application for developers to devise tools for their clients is Opinion Analysis API.
The algorithms use AI to identify and analyze emotions in texts. They use a variety of factors, including word choice, sentence structure, and punctuation. Sentiment analysis can be performed automatically, a fact that guarantees accuracy, efficiency and functionality.
In addition to identifying the positive or negative nature of a text, sentiment analysis can also be used to determine the strength of a feeling. This can be helpful for a variety of purposes, including marketing, advertising, and customer service. Sentiment analysis can be performed on both written and spoken text. However, written text is easier to analyze than spoken text because it´s more structured and consistent. In addition, sentiment analysis can be performed on both informal and formal texts.
The best tool to extract sentiments from unstructured text is Sentiment Analysis API. It’s easy-to-use and intuitive. This software uses cutting-edge AI to analyze the emotions in texts. It can be used for one´s personal needs or for one´s business. It retrieves results in JSON format, so it`s easily integrated into any projects! It can be used in any programming language.
Opinion Analysis API -together with analyze sentiment with natural language API, NLP sentiment analysis API, text sentimentanalysis API and API for sentiment analysis-
has grown in importance over the past few years as the volume of data available to businesses continues to grow. In fact, according to a recent survey, 90% of businesses are already using or planning to use sentiment analysis in the next two years.
This type of analysis can be used to identify customer pain points and areas for improvement, to predict future trends and identify opportunities for growth. It can also be used to monitor competitors and identify weaknesses that can be exploited, and definitely to better understand customers´views.
One of the most common ways to collect this information is through reviews or other forms of unstructured data. However, these reviews are often long and contain many different opinions. It can be difficult to determine which opinions are positive, negative, or neutral. This is where a sentiment analysis API comes in.
How To Get Started With A Sentiment Analysis API
Counting 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”: “I enjoyed the service” in the endpoint, the response will look like this:
{
"id": "1",
"predictions": [
{
"probability": 1,
"prediction": "Promote"
}
{
"id": "2",
"predictions": [
{
"probability": 1,
"prediction": "Promote"
}
{
"id": "3",
"predictions": [
{
"probability": 1,
"prediction": "Detract"
}
{
"id": "4",
"predictions": [
{
"probability": 1,
"prediction": "Indifferent"
}