  There is little prior work on quantifying the relationships between facial
expressions and emotionality in American Sign Language. In this final report,
we provide two methods for studying these relationships through probability and
prediction. Using a large corpus of natural signing manually annotated with
facial features paired with lexical emotion datasets, we find that there exist
many relationships between emotionality and the face, and that a simple
classifier can predict what someone is saying in terms of broad emotional
categories only by looking at the face.
