Black-Box Emotion Detection: on the Variability and Predictive Accuracy of Automated Emotion Detection Algorithms

The current research demonstrates considerable variability in predictive accuracy across major emotion detection systems (such as Google ML or Microsoft Cognitive Services) with lower (higher) classification accuracy for negative (positive) discrete emotions. We provide two modelling strategies to improve prediction accuracy by either combining feature sets or using ensemble methods.


Francesc Busquet and Christian Hildebrand (2020) ,"Black-Box Emotion Detection: on the Variability and Predictive Accuracy of Automated Emotion Detection Algorithms", in NA - Advances in Consumer Research Volume 48, eds. Jennifer Argo, Tina M. Lowrey, and Hope Jensen Schau, Duluth, MN : Association for Consumer Research, Pages: 831-835.


Francesc Busquet, University of St.Gallen
Christian Hildebrand, University of St. Gallen


NA - Advances in Consumer Research Volume 48 | 2020

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