Abstract
We trained a model to distinguish an extreme high arousal, unpleasant drink from regular drinks based on a range of implicit behavioral and physiological responses to naturalistic tasting. The trained model predicted arousal ratings of regular drinks, highlighting the possibility to estimate affective experience without having to rely on subjective ratings.
Original language | English |
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Pages (from-to) | 849-856 |
Number of pages | 8 |
Journal | IEEE transactions on affective computing |
Volume | 14 |
Issue number | 1 |
Early online date | 19 Oct 2020 |
DOIs | |
Publication status | Published - Mar 2023 |
Keywords
- Affect sensing and analytics
- Customer experience measurement
- Nonverbal synthesis
- Physiological measures
- Tasting
- n/a OA procedure