# Decoding Humor-Induced Amusement via Facial Expression Analysis: Toward Emotion-Aware Applications
## 3 Results
### Predicting Humorous Annesment Intensity Using Video Characteristics and Smiling  
To identify the key features related to humorous amusement, we compared videos' properties (i.e., movement, saliency, semantic distance and normalized Google distance) and % Smile in neutral, funny and very funny videos. Note that the videos were different for each participant depending on their evaluation (neutral, funny or very funny). We classified video types using a Random Forest algorithm at three specific moments in time: the beginning, middle and end of the viewing time window. These classifications led to significant predictions of amusement intensity [low, medium, and high] (above permutations threshold \(p\)'s < 0.01 and probabilistic chance level of 33% for three classes) and displayed mean DA of 62.1% \(\pm\) 5.0 for the beginning of the video, 62.4% \(\pm\) 5.6 for the middle part and 64.1% \(\pm\) 5.4% at the end (DA and SD values were computed across bootstraps). An ANOVA showed no significant difference between DAs across time (t (3,10) = 0.417, \(p\) = 0.662). Figure 7A and Table 1 show that the importance of % Smile varied with time to reach its utmost importance at the end of the video, corresponding to the punchline in funny videos. On the other hand, movement, saliency, and semantic distance were the most important contributors to classification at any point in time. However, their contribution did not vary over time, suggesting that these features are more related to the intrinsic properties of the neutral, funny, and very funny video groups and did not predict humorous amusement per se. This idea is supported by the confusion matrix (Figure 7B) in which we find that our models distinguished neutral videos with very high accuracy (accuracy = 83%) but made errors when predicting funny (accuracy = 37%) and very funny (accuracy = 70%) videos. These results suggest that the properties of neutral and humorous videos differed but were not sufficient to predict the intensity of amusement experienced by participants. These results also suggests that only the % Smile variable was relevant in this classification. Finally, we also performed the same classification (neutral, funny, very funny) using all available AUs, and were able to show that only AUs 6 and 12 (represented by the % Smile variable) contributed significantly and equally to the prediction of amusement over time. In order to further characterize the importance of % Smile in humorous amusement, we replicated the same classification approach but now to predict three different levels of amusement (low, moderate and high) over time while the neutral videos were removed. These classifications led to significant predictions of amusement intensity (above permutations threshold \(p\)'s > 0.009 for the middle and end of the video), and displayed mean DA of 41.1% \(\pm\) 2.8 for the beginning of the video, 44.0% \(\pm\) 3.1 for the middle part and 45.2% \(\pm\) 4.4 for the end. An ANOVA showed a significant difference between the three sections of the video (F (3,10) = 3.395, \(p\) < 0.001). Post hoc [PERSON]'s tests showed that middle