HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence
Benjamin Wortman and James Z. Wang
The Pennsylvania State University, USA
Abstract:
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.
Full Paper
(PDF, with supplementary materials, 16MB)
Citation:
Benjamin Wortman and James Z. Wang, ``HICEM: A High-Coverage Emotion Model for
Artificial Emotional Intelligence,'' IEEE Transactions on Affective Computing, vol. ,
no. , pp. -, 2024.
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Last Modified:
October 12, 2023
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