  Understanding, modelling and predicting human risky decision-making is
challenging due to intrinsic individual differences and irrationality. Fuzzy
trace theory (FTT) is a powerful paradigm that explains human decision-making
by incorporating gists, i.e., fuzzy representations of information which
capture only its quintessential meaning. Inspired by Broniatowski and Reyna's
FTT cognitive model, we propose a computational framework which combines the
effects of the underlying semantics and sentiments on text-based
decision-making. In particular, we introduce Category-2-Vector to learn
categorical gists and categorical sentiments, and demonstrate how our
computational model can be optimised to predict risky decision-making in groups
and individuals.
