Mood identification

Learn about how EDAA™ categorizes moods.

Mood identification is a comprehensive tool that categorizes various mood states and emotional responses. It is instrumental in analyzing and interpreting the emotional context of user interactions, enabling Affective Computing (powered by Virtue) to respond appropriately to different emotional states.

Advantage

The Moods dictionary enables you to offer a higher level of personalization in your solutions by providing empathetic user interactions based on a deep understanding of different moods and emotions.

Affective Computing (powered by Virtue) can provide automated personalization of human interactions with machines through any interface and in any environmental context.

Use case

In the domain of mental health and wellness, this functionality can be used to accurately assess user emotions, providing more effective support and recommendations.

How it works

Each interaction that Affective Computing (powered by Virtue) has with an end-user results in a user response of which the meaning could be interpreted literally or non-literally.

While it is easy to understand the literal meaning (for example, if a user's response is “Hey, I’m fine.”, its literal meaning is that the end-user is doing well), the non-literal meaning is derived from the user’s emotional state, which is hidden.

When Affective Computing (powered by Virtue) analyzes user responses, the literal and non-literal aspects are linked in a way that also takes into account the surrounding context (where the interactions occur). A dictionary of already-seen "situations" is created, which links the literal and non-literal parts through clustering the moods (unsupervised learning). This enables Affective Computing can determine the correct state of the user during each interaction.