An experimental comparison of two machine learning approaches for emotion classification

• Main Authors: ZHAO, Wangchuchu, SIAU, Keng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Emotion classification; Facial expression; Sexes; Machine learning.; Applied Behavior Analysis
• Online Access: https://ink.library.smu.edu.sg/sis_research/9438
• Institution: Singapore Management University

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.