TY - GEN
T1 - An experimental comparison of two machine learning approaches for emotion classification
AU - Zhao, Wangchuchu
AU - Siau, Keng
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2017
Y1 - 2017
N2 - Correctly identifying an emotion has always been challenging for humans, not to mention machines! In this research, we use machine learning to classify human emotion. Emotional differences between genders are well documented in fields like psychology. We hypothesize that genders will impact the accuracy of classifying emotion with machine learning. Two different machine learning approaches were tested in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, the genders were separated and two separate machines were used to learn the emotions of the two genders. Our preliminary results show that the approach where the genders were separated produces higher accuracy in classifying emotion.
AB - Correctly identifying an emotion has always been challenging for humans, not to mention machines! In this research, we use machine learning to classify human emotion. Emotional differences between genders are well documented in fields like psychology. We hypothesize that genders will impact the accuracy of classifying emotion with machine learning. Two different machine learning approaches were tested in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, the genders were separated and two separate machines were used to learn the emotions of the two genders. Our preliminary results show that the approach where the genders were separated produces higher accuracy in classifying emotion.
KW - Emotion classification
KW - Facial expression
KW - Machine learning
KW - Sexes
UR - https://www.scopus.com/pages/publications/85048373161
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85048373161&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9780996683142
VL - 2017-August
T3 - AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation
BT - AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation
PB - Americas Conference on Information Systems
T2 - America's Conference on Information Systems: A Tradition of Innovation, AMCIS 2017
Y2 - 10 August 2017 through 12 August 2017
ER -