How it works

Text to Emotion API uses complex NLP algorithms to mine underlying emotions from a given text. Basically the flow is as follows:

• Remove the unwanted textual part from the message.
• Perform the natural language processing techniques.
• Bring out the well pre-processed text from the text pre-processing.
• Find the appropriate words that express emotions or feelings.
• Check the emotion category of each word.
• Store the count of emotions relevant to the words found.

The output of the endpoint is a JSON dictionary with keys as emotion categories (happy, surprise, angry, sad, fear) and values as emotion scores. The score is a value between 0.00 to 1.00. The higher the value, the more likely our text to express that emotion.

Use cases

The API is mostly useful for the following industrial use cases:

• Customer Engagement: The signals that will be provided by the text to emotion API can help customer services teams to understand and interact with their clients more appropriately. It helps to speed up the process of understanding customer needs and complaints.
• Customer support with chatbots: Customer support with chatbots: Chatbots help companies to provide 24-hour support to their users. By enabling emotion detection, chatbots will be able to provide more human-like interaction which will help to increase customer satisfaction.
• Social Media Monitoring: Keeping a close eye on the social media posts will help brands to take action before small problems happen to be a crisis.

Set side properties your Api_key in TexttoEmotion_IS module

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