A user study with 20 participants found that linguistic analysis is more reliable than facial recognition for detecting emotions in proactive AI agents due to users displaying neutral 'poker faces,' while also showing that such agents can elicit emotions but risk disengagement if proactivity is unca
Computational Intelligence and Neuroscience , volume = 2022, number = 1, pages = 8032673, doi =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.HC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Evaluating multimodal emotion recognition in proactive conversational agents: A user study
A user study with 20 participants found that linguistic analysis is more reliable than facial recognition for detecting emotions in proactive AI agents due to users displaying neutral 'poker faces,' while also showing that such agents can elicit emotions but risk disengagement if proactivity is unca