Linking emotions to behaviors through deep transfer learning
Table 3: Behavior binary classification accuracy in percentage for context-dependent behavior recognition model from emotion labels
['[BOLD] Average', 'Acceptance', 'Blame', 'Positivity', 'Negativity', 'Sadness']
Considering that the classification accuracy can reach up to 50% by chance with balanced data, our results show that behavioral states can be weakly inferred from the emotion label vector sequences. Further, we perform the McNemar test, and the results above and throughout the paper are statistically significant with p<0.01. Despite the low accuracy of the behavior positivity, these results suggest a relationship between emotions and behaviors that we investigate further below.