# Decoding Humor-Induced Amusement via Facial Expression Analysis: Toward Emotion-Aware Applications
## 3 Results
### Predicting Humorous Annesment Intensity Using Video Characteristics and Smiling
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Average Feature Contribution (STD)**} \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{**Features**}} & \multicolumn{2}{c}{**All Videos**} & \multicolumn{2}{c}{**Humorous Video**} \\  & \multicolumn{2}{c}{**(Classes: Neutral/Funny/Very Funny)**} & \multicolumn{2}{c}{**(Classes: Low/Moderate/High)**} \\ \hline  & Beginning & Middle & End & Beginning & Middle & End \\ Movement & 0.318 (0.008) & 0.328 (0.006) & 0.220 (0.018) & 0.209 (0.033) & 0.213 (0.011) & 0.171 (0.030) \\ Saliency & 0.291 (0.009) & 0.269 (0.007) & 0.282 (0.006) & 0.248 (0.022) & 0.213 (0.019) & 0.142 (0.039) \\ Semantic Distance & 0.294 (0.011) & 0.248 (0.007) & 0.237 (0.006) & 0.223 (0.023) & 0.191 (0.022) & 0.106 (0.036) \\ NGD & 0.059 (0.006) & 0.047 (0.002) & 0.052 (0.003) & 0.089 (0.010) & 0.046 (0.007) & 0.037 (0.009) \\ Smile & 0.037 (0.021) & 0.107 (0.012) & 0.209 (0.018) & 0.232 (0.053) & 0.337 (0.042) & 0.543 (0.098) \\ Mean Accuracy & 62.1\% (5.0\%) & 62.3\% (5.6\%) & 64.1\% (5.4\%) & 41.0\% (2.8\%) & 43.9\% (3.1\%) & 45.2\% (4.6\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature contributions to models and decoding accuracy. This table illustrates how each feature contributed to predictive models (average \(\pm\) std), focusing on the beginning, the middle and the end of videos. Average contributions and corresponding decoding accuracies are represented using all videos (left) and humorous videos only (right). (\(p=0.05\)) and final (\(p=0.05\)) DAs were higher than DA for the beginning of the video. In this case, % Smile was the largest contributing feature (Figure 7C and Table 1) and varied with time to peak at the end of the video, as in the first classification (neutral, funny, very funny). As predicted, the importance of video property features was very low and constant over time. The confusion matrix (Figure 7D) shows that the algorithm was able to predict low (accuracy = 50%) and high (accuracy = 69%) levels of amusement, but struggled for moderate amusement (accuracy = 33%). These two classifications suggest that % Smile is the most relevant factor to significantly predict up to three different intensities of humorous amusement over time, with an increasing importance at the end of the video when the punchline occurs. Figure 7: Evolution of feature importance for predicting amusement intensity (**A**) shows the evolution of feature importance when predicting amusement for all videos. Evolution in the video (beginning, middle, end) of the contribution of each feature to the model decoding accuracy. Features observed are smiles (blue), normalized Google distance (orange), semantic distance (green), movement (red) and saliency (purple). (**B**) Confusion matrix from the best model obtained while trying to predict amusement on all videos (accuracy = 73%). The best model was during the end part of the video. (**C**) shows the evolution of feature importance when predicting amusement for humorous videos only. (**D**) Confusion matrix from the best model obtained while trying to predict funniness on humorous video only (accuracy = 51%). The best model was during the end part of the video.