emotion analysis

Sometimes the three classes of sentiment (positive, negative and neutral) are not sufficient to understand the nuances regarding the underlying tone of a sentence. Our Emotion Analysis classifier is trained on our proprietary dataset and tells whether the underlying emotion behind a message is: Happy, Sad, Angry, Fearful, Excited, Funny or Indifferent.

demo- enter a text

Happy

--

Angry

--

Excited

--

Sarcasm

--

Sad

--

Fear

--

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 emotion = pd.emotion('Did you hear the latest Porcupine Tree song ? It's rocking !');
System.out.println(emotion);
//Response
{
	"emotion": "happy"
}
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
emotion('Did you hear the latest Porcupine Tree song ? It's rocking !')
#Response
{
	"emotion": "happy"
}
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'
emotion('Did you hear the latest Porcupine Tree song ? Its rocking !')
#Response
{
	"emotion": "happy"
}
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 emotion = pd.emotion('Did you hear the latest Porcupine Tree song ? Its rocking !');
Console.WriteLine(emotion);
#Response
{
	"emotion": "happy"
}
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');
emotion('Did you hear the latest Porcupine Tree song ? Its rocking !');
#Response
{
	"emotion": "happy"
}
how our emotion analysis api works?

Emotion Detection API can accurately detect the emotion from any textual data. People voice their opinion, feedback and reviews on social media, blogs and forums.Marketers and customer support can leverage the power of Emotion Detection to read and analyze emotions attached with the textual data.
We use Deep Learning powered algorithms to extract features from the textual data. These features are used to classify the emotion attached to the data. We have trained our classifier using Convolutional Neural Networks (Covnets) on a tagged dataset created by our team.

read more
emotion analysis use cases

Brands can optimize their marketing and customer feedback efforts by reading online conversation about their products/services.With increased digitization, there are many avenues where brands can get feedback from customers in the form of text – like emails, chats and social media messages. Analyzing emotions attached to the textual data can let brands target users more efficiently. Read more use cases of Emotion Detection.

read more
why our emotion 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.