In my layman's experience, I'm vaguely aware there are four base emotions: happy, sad, afraid/surprised, and angry/disgusted.1
Some background: We're training an AI to learn the difference between happy voices and angry voices. We've had some success, by showing it 200 angry audio clips, 200 happy audio clips, and 200 neutral. It can now reasonably tell when we're talking pleasantly or confrontationally... but the accuracy could be better.
Our total training dataset is made up of these audio clips: Happy, angry, neutral, calm, sad, fearful, disgust, and surprised. I think we can be more accurate by including these emotions.
But this is the problem:
Happy/angry/neutral span opposite ends of a spectrum; like binary. It's easy to say:
Happy 1 Neutral 0 Angry -1
That's the shape of the data we need to train a neural network to recognize 'Happy'.
So the question would be, is there any 'right answer' on filling in these blanks? I've given it my best guesses below, but I'm hoping for something more scientific....
Happy 1 Angry -1 Neutral 0 Calm X (0.5?) Sad X (-1?) Fearful X (-0.5?) Disgust X (-0.75?) Surprised X (0.75?)