Pupil diameter (PD) has been suggested as a reliable parameter for identifying
an individual’s emotional state. In this paper, we introduce a learning
machine technique to detect and differentiate between positive and negative
emotions. We presented 30 participants with positive and negative sound
stimuli and recorded pupillary responses. The results showed a significant
increase in pupil dilation during the processing of negative and positive
sound stimuli with greater increase for negative stimuli. We also found a more
sustained dilation for negative compared to positive stimuli at the end of the
trial, which was utilized to differentiate between positive and negative
emotions using a machine learning approach which gave an accuracy of 96.5%
with sensitivity of 97.93% and specificity of 98%. The obtained results were
validated using another dataset designed for a different study and which was
recorded while 30 participants processed word pairs with positive and negative
emotions