sentiment analysis

Understand the social sentiment of your brand, product or service while monitoring online conversations. Sentiment Analysis is contextual mining of text which identifies and extracts subjective information in source material.

demo- Enter a text

Positive

--

Neutral

--

Negative

--

Something went wrong.

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 sentiment = pd.sentiment(Come on, let's play together');
System.out.println(sentiment);
//Response
{
	"probabilities": {
		"positive": 0.568817,
		"neutral": 0.400776,
		"negative": 0.030407
	}, 
	"sentiment":"positive"
}
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
sentiment( "Come on, lets play together" )
#Response
{
	"probabilities": {
		"positive": 0.568817,
		"neutral": 0.400776,
		"negative": 0.030407
	}, 
	"sentiment":"positive"
}
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'
sentiment( 'Come on, lets play together' )
#Response
{
	"probabilities": {
		"positive": 0.568817,
		"neutral": 0.400776,
		"negative": 0.030407
	}, 
	"sentiment":"positive"
}
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 sentiment = pd.sentiment('Come on, lets play together');
Console.WriteLine(sentiment);
#Response
{
	"probabilities": {
		"positive": 0.568817,
		"neutral": 0.400776,
		"negative": 0.030407
	}, 
	"sentiment":"positive"
}
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');
sentiment('Come on, lets play together');
#Response
{
	"probabilities": {
		"positive": 0.568817,
		"neutral": 0.400776,
		"negative": 0.030407
	}, 
	"sentiment":"positive"
}

# sentiment analysis

function name
paralleldots_sentiment
description

Using the function paralleldots_sentiment you can analyze any textual content and in return get the sentiment attached to the text.
Consider the following example where the text sentence “Team performed well overall” is being analyzed using paralleldots_sentiment.

example

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

HOW OUR SENTIMENT ANALYSIS API WORKS?

Sentiment analysis API provides a very accurate analysis of the overall emotion of the text content incorporated from sources like Blogs, Articles, forums, consumer reviews, surveys, twitter etc. Sentiment Analysis can be widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service.

It uses Long Short Term Memory (LSTM) algorithms to classify a text blob's sentiment into positive and negative. 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.

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sentiment analysis use cases

Sentiment Analysis can come out as the ultimate tool for marketing. You can leverage the power of sentiment analysis to read and analyze the sentiments of online conversation about your brand. It can also provide a fast solution to categorize your product reviews. You can identify influencers and categorize them as advocates or detractors of your brand. Additionally, Sentiment Analysis can aid you in market and competitive research.

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why our sentiment 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.