  As social robots and other intelligent machines enter the home, artificial
emotional intelligence (AEI) is taking center stage to address users' desire
for deeper, more meaningful human-machine interaction. To accomplish such
efficacious interaction, the next-generation AEI need comprehensive human
emotion models for training. Unlike theory of emotion, which has been the
historical focus in psychology, emotion models are a descriptive tools. In
practice, the strongest models need robust coverage, which means defining the
smallest core set of emotions from which all others can be derived. To achieve
the desired coverage, we turn to word embeddings from natural language
processing. Using unsupervised clustering techniques, our experiments show that
with as few as 15 discrete emotion categories, we can provide maximum coverage
across six major languages--Arabic, Chinese, English, French, Spanish, and
Russian. In support of our findings, we also examine annotations from two
large-scale emotion recognition datasets to assess the validity of existing
emotion models compared to human perception at scale. Because robust,
comprehensive emotion models are foundational for developing real-world
affective computing applications, this work has broad implications in social
robotics, human-machine interaction, mental healthcare, and computational
psychology.
