So you may be familiar with Word2Vec, (W2V) which as Wikipedia describes1 "captures the linguistic contexts of words" using vector arithmetic. For example, subtract 'Paris' from 'France' and add 'Italy' and you get 'Rome'.
What you need is something like a Sentiment2Vec (S2V) that captures the similarities between emotional transitions. Something like: subtract 'fear' from 'sadness', add 'joy' and you get 'hope'. Or: subtract 'sting' from 'papercut', add 'smashed' and you get 'throbbing'.
The catch is that you don't have an easily accessible corpus of emotional contexts to train with, like you have with words. If you had a million hours of fMRI - mapping the transitions between emotions in hundreds of subjects - then you could use that data to build an S2V. You probably don't have that data though.
In the mean time, you could just build a W2V that specializes in sentiment. You could even try to use a current sentiment analysis engine to bootstrap it. Perhaps if you read enough text that says "I got a papercut and it stings" and "I smashed my finger and it's throbbing" then you could eventually produce an S2V. Children's books often use explicit language regarding emotional context ("this made the boy feel sad").
But words are still a far cry from the experiential context that a connectome map would provide. To test whether you have something useful or not, you might want to implement your S2V in a mouse foraging simulation - see whether it produces typical behavior and if any cooperative or competitive dynamics can organically grow out of your S2V.
Some further info on the subject:
In 2014, Glasgow University claimed2 that there are four primary emotions: happiness, sadness, fear and anger.
This website3 provides nice (if somewhat short) hierarchical breakdown of secondary and tertiary emotions under primary emotions.