# Multitask Learning-Based Affective Prediction for Videos of Films and TV Scenes
## 4 Experimental Results
### Results
#### 4.3.4 Comparison with Other Algorithms  
Table 10 lists the results of the different comparison methods on the self-constructed dataset. We selected the classical multitask learning framework Share-Bottom, the classical gated network PLE model, the approach of [PERSON] et al. (2012), which uses a CNN as a shared layer for sentiment analysis, and the MMTrans-MT network proposed by [PERSON] et al. (2014), which uses 4 Transformer encoders as a shared layer for sentiment analysis. To compare with the MHAF-PLE model proposed in our paper, the experiments are conducted in the same experimental environment, and the same features are used for comparison. The results show that MHAF-PLE has the best performance, with improvements in the average absolute error for both fine-grained sentiment prediction for the primary task and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotion Task** & **Weight Consistency** & **FA** & **FA + Loss Weight** &
\begin{tabular}{c} **FA + Loss Weight +** \\ **Amplification Factor** \\ \end{tabular} \\ \hline Warm & 0.0240 & 0.0210 & 0.0210 & 0.0185 \\ Hopeful & 0.0545 & 0.0488 & 0.0455 & 0.0465 \\ Happy & 0.0518 & 0.0452 & 0.0447 & 0.0394 \\ Romantic & 0.0489 & 0.0482 & 0.0488 & 0.0504 \\ Relaxed & 0.0586 & 0.0508 & 0.0475 & 0.0465 \\ Fresh & 0.0519 & 0.0521 & 0.0510 & 0.0501 \\ Cozy & 0.0447 & 0.0567 & 0.0587 & 0.0444 \\ Sunny & 0.0525 & 0.0545 & 0.0538 & 0.0523 \\ Magnificent & 0.0505 & 0.0585 & 0.0529 & 0.0498 \\ Dreamy & 0.0541 & 0.0507 & 0.0472 & 0.0456 \\ Lonely & 0.0479 & 0.0430 & 0.0427 & 0.0433 \\ Sentimental & 0.0452 & 0.0369 & 0.0343 & 0.0328 \\ Disappointed & 0.0542 & 0.0423 & 0.0419 & 0.0418 \\ Depressed & 0.0433 & 0.0429 & 0.0381 & 0.0352 \\ Oppressive & 0.0464 & 0.0614 & 0.0589 & 0.0586 \\ Anxious & 0.0686 & 0.0469 & 0.0451 & 0.0371 \\
**Average** & 0.0500 & 0.0474 & 0.0457 & **0.0432** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction results based on different loss functions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**k** & **MAE of Affective Prediciton** \\ \hline
1 & 0.0432 \\
3 & 0.0389 \\
4 & 0.0388 \\
5 & **0.0361** \\
6 & 0.0387 \\
7 & 0.0403 \\
8 & 0.0412 \\
9 & 0.0432 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Prediction results based on different settings of k.  
character categorization for the secondary task. The MAE of MHAF-PLE is reduced by 0.1800, 0.1362, 0.1534, and 0.1538 compared to Share-Bottom, PLE, RF [12], and MMTrans-MT [14] models, respectively; in addition, the classification accuracy for the secondary task is also improved by 17%, 18%, and 43%, 39% compared to Share-Bottom, PLE, RF [12], and MMTrans-MT [14] models, respectively. The MAE for each sentiment is also reduced, which suggests that compared to Share-Bottom and PLE models, RF [12], and MMTrans-MT [14], the proposed method performs well in fine-grained affective analysis.  
The MHAF-PLE model integrates the attention mechanism and gives higher weight to information rich in both video and images, enabling better mining of emotional feature representation. The constraint design based on factor association can adjust the learning speed and proportion through the loss function according to the characteristics of fine-grained affects, improving the accuracy of prediction.