An Experimental Comparison of Two Machine Learning Approaches for Emotion Classification

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. Keywords Emotion classification, facial expression, sexes, machine learning

Machine Learning Approaches to Sentiment Analytics

One key aspect of sentiment analytics is emotion classification. This research studies the use of machine learning approaches to classify human emotion. Two different machine learning approaches were compared in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, genders were separated and two separate machines were used to learn the emotions of the two genders. We also manipulated the training sample sizes and study the effect of training sample sizes on the two machine learning approaches. Our preliminary results show that the approach where the genders were separated produces a higher accuracy in classifying emotions. We also observe that training sample sizes have different impact on the two approaches