intent analysis

This classifier tells whether the underlying intention behind a sentence is opinion, news, marketing, complaint, suggestion, appreciation, and query. This is trained on our proprietary dataset.

demo- enter a text

News

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Feedback

--

Query

--

Marketing

--

Spam

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Ready to Integrate? Check out the API wrappers below

For setup and installation instruction, please visit our Github Page
import paralleldots.ParallelDots;
// Get your API key here
ParallelDots pd = new ParallelDots("<YOUR_API_KEY>");
import paralleldots.ParallelDots;
ParallelDots pd = new ParallelDots();
String intent = pd.intent("Finance ministry calls banks to discuss new facility to drain cash");
System.out.println(intent);
//Response
{
		"probabilities": {
			"news": 0.946028, 
			"other": 0.015853, 
			"query": 0.000412, 
			"feedback/opinion": 0.014115, 
			"spam": 0.023591
		}
}
For setup and installation instruction, please visit our Github Page
from paralleldots import set_api_key, get_api_key
# Get your API key here
set_api_key(<YOUR_API_KEY>)
get_api_key()
from paralleldots import similarity, ner, taxonomy, sentiment, keywords, intent, emotion, multilang, intent
intent("Finance ministry calls banks to discuss new facility to drain cash")
#Response
{
		"probabilities": {
			"news": 0.946028, 
			"other": 0.015853, 
			"query": 0.000412, 
			"feedback/opinion": 0.014115, 
			"spam": 0.023591
		}
}
For setup and installation instruction, please visit our Github Page
require 'paralleldots'
# Get your API key here
set_api_key(<YOUR_API_KEY>)
get_api_key()
require 'paralleldots'
intent('Finance ministry calls banks to discuss new facility to drain cash')
#Response
{
		"probabilities": {
			"news": 0.946028, 
			"other": 0.015853, 
			"query": 0.000412, 
			"feedback/opinion": 0.014115, 
			"spam": 0.023591
		}
}
For setup and installation instruction, please visit our Github Page
using ParallelDots
# Get your API key here
ParallelDots.api pd = new ParallelDots.api("<YOUR_API_KEY>");
var intent = pd.intent("Finance ministry calls banks to discuss new facility to drain cash");
Console.WriteLine(intent);
#Response
{
		"probabilities": {
			"news": 0.946028, 
			"other": 0.015853, 
			"query": 0.000412, 
			"feedback/opinion": 0.014115, 
			"spam": 0.023591
		}
}
For setup and installation instruction, please visit our Github Page
require(__DIR__ . '/vendor/paralleldots/apis/autoload.php');
# Get your API key here
set_api_key("<YOUR_API_KEY>");
get_api_key();
require(__DIR__ . '/vendor/paralleldots/apis/autoload.php');
intent('Finance ministry calls banks to discuss new facility to drain cash');
#Response
{
		"probabilities": {
			"news": 0.946028, 
			"other": 0.015853, 
			"query": 0.000412, 
			"feedback/opinion": 0.014115, 
			"spam": 0.023591
		}
}
how our intent analysis api works?

Intent Analysis goes a level deeper than sentiment analysis and gives an idea of whether a string of text is a complaint, a suggestion or a query.Gauging the intent of messages on social media opens a lot of new possibilities.
It uses Long Short Term Memory (LSTM) algorithms to classify a text into different. LSTMs model sentences as chain of forget-remember decisions based on context. It is trained on social media data and news data differently for handling casual and formal language. We also have trained this algorithm for various custom datasets for different clients.

intent analysis use cases

Intent Analysis can help brands classify online conversation as complaint, suggestion or a query. Marketers can target customers based on their intention about a feature or a product. Thus, optimizing their advertising funnel. Customer support department of a brand can be benefited heavily using Intent Analysis. They can automate classification of online conversations and make the process fast and efficient using our Intent Analysis API. Read more about application of Intent Analysis.

read more
why our intent analysis api ?
Accurate

Highly accurate classification of unstructured textual data.

Real Time

State of the art technology to provide accurate results real-time.

Customizable

Can be trained on custom dataset to obtain similar accuracy and performance.