# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 4 Experimental Results
### Advancements Brought by CA-WGNN  
The diagram depicts the architecture of the CA-WGNN model, highlighting the sequential data flow through various layers, each contributing significantly to the model's classification capabilities. * **Cumulative Attribute Weighting:** The CA-WGNN model introduces a novel cumulative weighting approach for attributes within a graph structure. This technique is instrumental in deepening the understanding of node interrelations and dependencies within the graph. * **Enhanced Contextual Understanding:** By focusing on cumulative attributes, CA-WGNNs achieve a heightened comprehension of the broader context surrounding each node. This aspect is crucial for making accurate predictions in complex graph structures. Figure 14: Graphical representation of F1-score. \begin{table}
\begin{tabular}{l c} \hline \hline
**Dataset** & **F1-Score (\%)** \\ \hline IEMOCAP (Dataset 1) & 0.93 \\ CMU-MOSI (Dataset 2) & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of F1-score. * **Applicability in Complex Network Analysis:** CA-WGNNs are particularly advantageous in scenarios involving intricate graph structures. In such contexts, a node's influence often extends beyond its immediate neighbors, necessitating a more sophisticated analysis approach. The effectiveness of the CA-WGNN model is further evidenced by its performance metrics on the IEMOCAP dataset, as shown in the Table 13. The model demonstrates high precision, recall, and F1-score across different categories, underscoring its robustness and accuracy in sentiment analysis. Notably, the model achieves an overall accuracy of 94%, with a balanced performance across all categories, as indicated by the macro and weighted averages. These results highlight the model's capability in accurately classifying sentiments, even in the challenging context of multimodal datasets. The CA-WGNN model's performance was rigorously assessed using the CMU-MOSI dataset (Table 14), known for its comprehensive and multimodal nature. This evaluation aimed to ascertain the model's proficiency in accurately analyzing and classifying sentiments across diverse categories. The table below presents a detailed breakdown of the model's performance metrics, including precision, recall, and F1-score for Negative, Neutral, and Positive sentiment categories, along with the overall accuracy, macro, and weighted averages. The data in the table reveal the CA-WGNN model's exceptional accuracy and consistency in sentiment classification. The model demonstrates high precision and recall rates across all categories, a testament to its robust algorithmic structure. The precision of 0.97 in the Neutral category underscores the model's capability to discern nuanced emotional expressions, a challenging aspect in affective computing. The overall accuracy of 94% is indicative of the model's effectiveness in dealing with the intricacies of multimodal data, as presented in the CMU-MOSI dataset. These results validate the CA-WGNN model's theoretical underpinnings and establish its practical applicability in real-world scenarios, particularly in the domain of multimodal affective computing. The comprehensive performance analysis using the CMU-MOSI dataset offers valuable insights into the model's potential for broader applications in sentiment analysis and emotional recognition. The table above provides a quantitative evaluation of the CA-WGNN model's performance. The reported metrics, namely precision, recall, and F1-score, reflect the model's effectiveness in various sentiment categories (Negative, Neutral, and Positive). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Negative & 0.86 & 0.93 & 0.90 & 69 \\ Neutral & 0.96 & 0.97 & 0.96 & 68 \\ Positive & 0.96 & 0.87 & 0.92 & 63 \\ \hline
**Accuracy** & & & 0.93 (200) & \\ \hline
**Macro Average** & & & 0.93 & 0.93 \\
**Weighted Average** & & & 0.93 & 0.93 \\ \hline \hline \end{tabular}
\end{table}