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 or Bored.

Emotion API works in fourteen different languages mentioned here.

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Try our free demo now by typing a sentence or choose from the options in the drop-down menu.

Select a Language
Emotion Analysis

Happy

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Emotion Analysis

Angry

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Emotion Analysis

Excited

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Emotion Analysis

Sad

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Emotion Analysis

Fear

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Emotion Analysis

Bored

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You might also be interested in these APIs: Sentiment Analysis, Intent Analysis, Named Entity Recognition

Why our Emotion Analysis API ?

Accurate

Komprehend Emotion Analysis API maintains high accuracy in real world, and detects subjective emotions like Sarcasm from text.

Fast

Process and return results in extremely short time, meeting demands from various industries.

Flexible Deployment

Komprehend Emotion Analysis support private cloud deployments via Docker containers or on-premise deployment ensuring no data leakage.

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.

use cases

Target detractors to improve service to them

By capturing customers who feel strongly negative towards your product or service, customer service can deal with their issues specifically. Imagine the fury of a customer who leaves a comment that’s 0.95 negative. In such a case, prioritizing user’s complaints using Emotion Analysis score can help you in taking actions quickly.

Brand-watching

Enterprises can analyze how people are reacting to their marketing campaigns on social media platforms like Youtube, Facebook, Twitter, and Instagram. This gives them an objective feedback about the content that works and empowers them in fine tuning their marketing strategy accordingly.

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Custom Solutions

Want to train your own custom model? Contact Sales to get started

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