  Predicting the outcome of a process requires modeling the system dynamic and
observing the states. In the context of social behaviors, sentiments
characterize the states of the system. Affect Control Theory (ACT) uses
sentiments to manifest potential interaction. ACT is a generative theory of
culture and behavior based on a three-dimensional sentiment lexicon.
Traditionally, the sentiments are quantified using survey data which is fed
into a regression model to explain social behavior. The lexicons used in the
survey are limited due to prohibitive cost. This paper uses a fine-tuned
Bidirectional Encoder Representations from Transformers (BERT) model to develop
a replacement for these surveys. This model achieves state-of-the-art accuracy
in estimating affective meanings, expanding the affective lexicon, and allowing
more behaviors to be explained.
