Data_Sheet_2.zip

Abstract

Even though culture has been found to play some role in negative emotion expression, affective computing research primarily takes on a basic emotion approach when analyzing social signals. Furthermore, automatic negative emotion recognition systems still train data that originates primarily from North America and contains a majority of Caucasian training samples. As such, the current study aims to address this problem by leveraging machine learning models to classify 3 negative emotions, contempt, anger and disgust (CAD) amongst 3 different cultures: North American, Persian, and Filipino. Using a curated dataset compiled from YouTube videos, a support vector machine (SVM) was used to predict negative emotions amongst differing cultures. In addition a one-way ANOVA was used to analyse the differences that exist between each culture group in-terms of level of activation of underlying social signal. Our results indicated that there are significant differences in level of activation of underlying social signals. Despite that, while the North American expressions of CAD were well recognized, Filipino and Persian expressions were recognized at near chance levels.