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.
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.
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
Highly accurate classification of unstructured textual data
State of the art technology to provide accurate results real-time
Can be trained on custom dataset to obtain similar accuracy and performance