text classification

Text Classification can be useful in understanding customer behaviour by categorizing conversations on social networks, feedback and other web sources. Search engines, newspapers, or e-commerce portals categorize their content or products to facilitate the search and navigation.

demo- enter a text
Our suggested tags:

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 taxonomy = pd.taxonomy('Narendra Modi is the prime minister of India');
System.out.println(taxonomy);
//Response
{
		"taxonomy":[
			{
				"tag": "terrorism",
				"confidence_score": 0.531435
			}, 
			{
				"tag": "world politics",
				"confidence_score": 0.391963
			},
			{
				"tag": "politics",
				"confidence_score": 0.358955
			}, 
			{
				"tag": "religion",
				"confidence_score": 0.308195
			}, 
			{
				"tag": "defense",
				"confidence_score": 0.26187
			},
			{
				"tag": "business",
				"confidence_score": 0.20885
			}, 
			{
				"tag": "automobile",
				"confidence_score": 0.001161
			}, 
			{
				"tag": "personal care",
				"confidence_score": 0.000275
			}
		]
	}
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, abuse
String taxonomy = pd.taxonomy('Narendra Modi is the prime minister of India');
System.out.println(taxonomy);
#Response
{
	"tag": "terrorism",
	"confidence_score": 0.531435
}, 
{
	"tag": "world politics",
	"confidence_score": 0.391963
},
{
	"tag": "politics",
	"confidence_score": 0.358955
}, 
{
	"tag": "religion",
	"confidence_score": 0.308195
}, 
{
	"tag": "defense",
	"confidence_score": 0.26187
},
{
	"tag": "business",
	"confidence_score": 0.20885
}, 
{
	"tag": "automobile",
	"confidence_score": 0.001161
}, 
{
	"tag": "personal care",
	"confidence_score": 0.000275
}
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'
String taxonomy = pd.taxonomy('Narendra Modi is the prime minister of India');
System.out.println(taxonomy);
#Response
{
	"tag": "terrorism",
	"confidence_score": 0.531435
}, 
{
	"tag": "world politics",
	"confidence_score": 0.391963
},
{
	"tag": "politics",
	"confidence_score": 0.358955
}, 
{
	"tag": "religion",
	"confidence_score": 0.308195
}, 
{
	"tag": "defense",
	"confidence_score": 0.26187
},
{
	"tag": "business",
	"confidence_score": 0.20885
}, 
{
	"tag": "automobile",
	"confidence_score": 0.001161
}, 
{
	"tag": "personal care",
	"confidence_score": 0.000275
}
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>");
String taxonomy = pd.taxonomy('Narendra Modi is the prime minister of India');
System.out.println(taxonomy);
#Response
{
	"tag": "terrorism",
	"confidence_score": 0.531435
}, 
{
	"tag": "world politics",
	"confidence_score": 0.391963
},
{
	"tag": "politics",
	"confidence_score": 0.358955
}, 
{
	"tag": "religion",
	"confidence_score": 0.308195
}, 
{
	"tag": "defense",
	"confidence_score": 0.26187
},
{
	"tag": "business",
	"confidence_score": 0.20885
}, 
{
	"tag": "automobile",
	"confidence_score": 0.001161
}, 
{
	"tag": "personal care",
	"confidence_score": 0.000275
}
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');
String taxonomy = pd.taxonomy('Narendra Modi is the prime minister of India');
System.out.println(taxonomy);
#Response
{
	"tag": "terrorism",
	"confidence_score": 0.531435
}, 
{
	"tag": "world politics",
	"confidence_score": 0.391963
},
{
	"tag": "politics",
	"confidence_score": 0.358955
}, 
{
	"tag": "religion",
	"confidence_score": 0.308195
}, 
{
	"tag": "defense",
	"confidence_score": 0.26187
},
{
	"tag": "business",
	"confidence_score": 0.20885
}, 
{
	"tag": "automobile",
	"confidence_score": 0.001161
}, 
{
	"tag": "personal care",
	"confidence_score": 0.000275
}

#text classification (taxonomy)

function name
paralleldots_taxonomy
description

Text Classification is a very powerful tool in understanding customer behavior by categorizing data sets on a large scale. You can assign categories to a text content using the paralleldots_taxonomy function, allowing to structure data for better insights.
Consider the following example where text input “Under the Uruguay Round, the national governments of all the member countries have negotiated improved access to the markets of the member countries so as to enable business enterprises to convert trade concessions into new business opportunities.” are assigned the relevant categories using the Text Classification API.

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

how our text classification api works?

Text Classification is a very important tool for categorisation of data sets on a large scale. Text Classification assigns one or more categories to a text, allowing to structure data for better insights. We utilize a comprehensive list of taxonomy to categorize the text content or web page contents into definitive tags.

Text Classification might involve different types of datasets and we have solutions for all the variations. A client might have datasets that are: 1. Untagged datasets, to be classified into generic categories. We have our API for classifying sentences/articles into standard IAB categories. 2. Tagged Datasets with multiple categories. This type of classifier can classify text into 10s and 100s of user defined categories like E-commerce product categories. 3. Partially tagged datasets, where only some of the data is tagged with categories and a lot of untagged data is available. Such classifiers are used to do intent analysis. 4. Chat Lingo and shorthand text classification. For such classification, where words are shortened and are not grammatically accurate, we use character level embeddings and hence can get around users' tendency to shorthand text.

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text classification use cases

Text classification can come in handy whenever there is a need of organizing large corpus. Platforms such as E-commerce, news agencies, content curators, blogs, directories, government, academia, and likes can use automated technologies to classify and tag content and products. Text classification can be customized heavily to aid highly selective situations involving analyzing and classifying texts.

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why our text classfication 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.