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

Item Type Conference or Workshop Item (Other)
Abstract

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.

Authors Busquet I Segui, Francesc
Language English
Keywords emotion, recognition, ai
Subjects business studies
computer science
other research area
HSG Classification contribution to scientific community
Date 2 October 2020
Event Title ACR Conference 2020
Event Location Paris
Event Dates 1-4th October
Depositing User Francesc Busquet I Segui
Date Deposited 23 Nov 2020 16:45
Last Modified 25 Feb 2021 01:26
URI: https://www.alexandria.unisg.ch/publications/261517

Download

Full text not available from this repository.

Citation

Busquet I Segui, Francesc: Black-Box Emotion Detection: On the Variability and Predictive Accuracy of Automated Emotion Detection Algorithms. [Conference or Workshop Item]

Statistics

https://www.alexandria.unisg.ch/id/eprint/261517
Edit item Edit item
Feedback?