## 4 Experimental Evaluation
### Quantitative Analysis
#### 4.4.2 Unaligned Settings  
The unaligned settings do not require explicit alignment of different modality data sequences. The experimental results are shown in Tables 3 and 4. Compared with other baseline models in unaligned settings, the UA-BFET model has the best performance under all four different evaluation metrics. Firstly, compared with the MISA model with optimal performance based on word alignment settings in the CMU-MOSI dataset, the proposed UA-BFET model improves the Acc-2 by 2.9% and the F1-Score by 3.0%. In the CMU-MOSEI dataset, based on the MSAF model with the best performance in word alignment settings, the UA-BFET model has decreased by 1% in Acc-2 and 1.6% in F1-Score. And it has the best performance in MAE and Corr. The experimental results show that the UA-BFET model, which adds the cyclic memory enhancement network across time steps and the feature enhancement technique of CCA, can solve the forgetting problem of unimodal sequence and hold better interaction between different modalities at important time steps. This is consistent with the research motivation of this paper. Secondly, based on the unaligned settings, the accuracy of the UA-BFET model is consistent with the overall best-performing Weighted Cross-Modal Attention Mechanism model in the CMU-MOSI dataset, with a 0.1% improvement in F1-Score and a 1.1% improvement in Corr evaluation index. As shown in Tables 3 and 4, it can be seen from the experimental results that the UA-BFET model can achieve the same or even better results than the fusion of three different modalities. In the CMU-MOSEI dataset, compared with the optimal Weighted Cross-Modal Attention Mechanism model, the Acc-2 of the UA-BFET model increased by 0.7%, and the F1-Score increased by 0.1%. In the experiment, we compared the experimental results of the UA-BFET model in the unaligned data scenarios with other word alignment and unaligned baseline models, respectively. It can be proved that the model performance improvement of the UA-BFET model is small compared with the baseline model in the unaligned setting, and the model performance improvement is significant when compared with the baseline model in the word alignment setting. The reason is that the number of parameters and computational complexity of the cross-modal sentiment analysis model are larger in the unaligned setting than in the word alignment setting. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc-2/\%** & **F1-Score/\%** & **MAE** & **Corr** \\ \hline \multicolumn{5}{c}{CMU-MOSEI (Word alignment)} \\ \hline EF-LSTM & 78.2 & 77.9 & 0.642 & 0.616 \\ LF-LSTM & 80.6 & 80.6 & 0.619 & 0.659 \\ MFM [8] & - & - & 0.568 & 0.717 \\ RAVEN [13] & 79.1 & 79.5 & 0.614 & 0.662 \\ HFFN [10] & 60.4 & 59.1 & - & - \\ MCTN [9] & 79.8 & 80.6 & 0.609 & 0.670 \\ MISA [11] & 83.6 & 83.8 & 0.555 & 0.756 \\ MSAF [42] & **85.5** & **85.5** & 0.559 & 0.738 \\ UA-BFET (ours) & 84.5 & 83.9 & **0.424** & **0.793** \\ \hline \multicolumn{5}{c}{CMU-MOSEI (unaligned)} \\ \hline MulT [14] & 81.6 & 81.6 & 0.591 & 0.694 \\ LMT-MulT [35] & 80.8 & 81.3 & 0.620 & 0.668 \\ LMR-CBT [15] & 80.9 & 81.5 & - & - \\ Self-MM [24] & 82.8 & 82.5 & 0.530 & 0.765 \\ UR-Transformer [17] & 81.8 & 81.8 & 0.597 & 0.671 \\ PMR [18] & 83.1 & 82.8 & - & - \\ Weighted Cross-modal & 83.8 & 83.8 & 0.547 & 0.751 \\ Attention Echnism [36] & **84.5** & **83.9** & **0.424** & **0.793** \\ UA-BFET (ours) & \multicolumn{1}{c}{} & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cross-modal sentiment analysis results in aligned / unaligned MOSEI data sets. The bold part represents the optimal values for all baseline models. In addition, the experiment uses the Dropout method to prevent overfitting. The value of the parameter Dropout affects the final output of the model. On the one hand, the larger the value of Dropout, the less overfitting the model will hold, and yet the model's generalization ability will also decrease. Because Dropout randomly discards some neurons, resulting in the loss of some important emotional information.