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As social robots and other intelligent machines enter the home, artificialemotional intelligence (AEI) is taking center stage to address users' desirefor deeper, more meaningful human-machine interaction. To accomplish suchefficacious interaction, the next-generation AEI need comprehensive humanemotion models for training. Unlike theory of emotion, which has been thehistorical focus in psychology, emotion models are a descriptive tools. Inpractice, the strongest models need robust coverage, which means defining thesmallest core set of emotions from which all others can be derived. To achievethe desired coverage, we turn to word embeddings from natural languageprocessing. Using unsupervised clustering techniques, our experiments show thatwith as few as 15 discrete emotion categories, we can provide maximum coverageacross six major languages--Arabic, Chinese, English, French, Spanish, andRussian. In support of our findings, we also examine annotations from twolarge-scale emotion recognition datasets to assess the validity of existingemotion models compared to human perception at scale. Because robust,comprehensive emotion models are foundational for developing real-worldaffective computing applications, this work has broad implications in socialrobotics, human-machine interaction, mental healthcare, and computationalpsychology.

HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence