Towards Emotion-Based Synthetic Consciousness: Using LLMs to Estimate
  Emotion Probability Vectors
David Sinclair Willem Pye
This paper shows how LLMs (Large Language Models) may be used to estimate a
summary of the emotional state associated with piece of text. The summary of
emotional state is a dictionary of words used to describe emotion together with
the probability of the word appearing after a prompt comprising the original
text and an emotion eliciting tail. Through emotion analysis of Amazon product
reviews we demonstrate emotion descriptors can be mapped into a PCA type space.
It was hoped that text descriptions of actions to improve a current text
described state could also be elicited through a tail prompt. Experiment seemed
to indicate that this is not straightforward to make work. This failure put our
hoped for selection of action via choosing the best predict ed outcome via
comparing emotional responses out of reach for the moment.