Random forest

Algorithm

Linguistics

Philosophy

Computer science

Emotion detection

Black box

Pattern recognition (psychology)

Emotion recognition

Feature (linguistics)

Artificial intelligence

Machine learning

ACR North American Advances

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 modeling strategies to improve prediction accuracy by either combining feature sets or using ensemble methods.

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