# Dynamic Tuning and Multi-Task Learning-Based Model for Multimodal Sentiment Analysis
## 4 Experiments
### Comparative Experiments  
Tables 2 and 3 summarize the results of the comparative experiments with various methods. The listed results are based on the average of five runs with different random seeds, with the performance data for all baseline models, except for ConFEDE, sourced from published studies. On the CH-SIMS dataset, DMMSA outperformed all baseline models in classification and regression tasks. Compared to ConFEDE, it achieved increases of 1.27% and 3.20% in Acc-3 and Acc-5, respectively. This is mainly due to the integrated coarse-grained sentiment analysis task, which boosts classification performance. DMMSA also showed significant progress in the MAE and Corr metrics. It effectively captured the interdependencies of uni- and multimodal sentiment analysis and embedded a coarse-grained task that constrainedthe prediction scope and simplified the analysis. As shown in Figure 4, DMMSA exhibited smaller MAE fluctuations and faster convergence during training than models without coarse-grained analysis, confirming its positive role in optimization. To further validate our approach, experiments were conducted on the MOSI and MOSEI datasets without unimodal sentiment labels. Table 3 shows that DMMSA surpassed all baselines, demonstrating excellent performance even without unimodal labels. This is mainly due to the design of the contrastive learning task. As shown in Figure 5, the model was still able to identify and separate uni- and multimodal features effectively under the guidance of this task. Notably, DMMSA's improvements in Acc-5, MAE, and Corr were more significant than those in Acc-2 and Acc-3. This is because complex tasks require higher model performance and feature quality. Simple tasks can yield good results with lower-level features, while DMMSA's advantage lies in extracting higher-quality features, making its performance gain more prominent as the task difficulty increases. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Acc-3 (\(\uparrow\))** & **Acc-5 (\(\uparrow\))** & **MAE (\(\downarrow\))** & **Corr (\(\uparrow\))** \\ \hline LF-DNN & 66.91 & 41.62 & 0.420 & 0.612 \\ MFN(A) & 65.73 & 39.47 & 0.435 & 0.582 \\ LMF & 64.68 & 40.53 & 0.441 & 0.576 \\ TFN & 65.12 & 39.30 & 0.432 & 0.591 \\ Mult(A) & 64.77 & 37.94 & 0.453 & 0.561 \\ Self-MM & 64.73 & 43.15 & 0.414 & 0.598 \\ ConFEDE & 68.36 & 43.72 & 0.3924 & 0.6351 \\ DMMSA & 69.63 & 46.92 & 0.3778 & 0.66 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of comparative experiment on the CH-SIMS dataset. “Acc” denotes “accuracy”. Figure 4: Evolution of the MAE over epochs for different optimization strategies on the MOSI validation dataset. “Cross” represents DMMSA incorporating the coarse-grained sentiment analysis task, whereas “No_Cross” corresponds to DMMSA with the coarse-grained sentiment analysis task removed. The circles indicate the lowest MAE values. To confirm this hypothesis, we designed an incremental experiment. Table 4 shows that DMMSA's performance in Acc-2 converged when trained with 60% of the data and did not improve with more data. In contrast, its performance in Acc-7 and regression tasks continuously improved as the amount of training data increased. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Acc-2** & **F1** & **Acc-7** & **MAE** & **Corr** \\ \hline MOSEI & 82.63/86.27 & 83.04/86.21 & 53.91 & 0.527 & 0.777 \\ MOSEI* 0.8 & 82.41/86.15 & 82.87/86.14 & 53.39 & 0.532 & 0.772 \\ MOSEI*0.6 & 83.77/86.12 & 84.01/85.99 & 52.77 & 0.538 & 0.769 \\ MOSEI*0.4 & 82.78/86.09 & 83.16/86.03 & 53.36 & 0.540 & 0.767 \\ MOSEI*0.2 & 80.68/85.03 & 81.27/85.06 & 52.37 & 0.551 & 0.760 \\ MOSEI*0.1 & 81.89/85.08 & 82.33/85.04 & 52.18 & 0.552 & 0.755 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of DMMSA under varying amounts of training data. Figure 5: Similarity between similarity and dissimilarity features. “CL” denotes the similarity of a model incorporating the contrastive learning task. “No_CL” represents the similarity of a model without the contrastive learning task.