# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 5 Discussion  
In the evolving field of emotion recognition, the IEMOCAP and CMU-MOSI datasets are essential, encompassing diverse text, audio, and video modalities. Their extensive multimodal data are crucial for those studying emotional expression. These datasets' varied participant demographics, emotional contexts, and data collection methods impact the applicability of models across different datasets, highlighting challenges in effectively merging data from these varied modalities. Traditional approaches often fall short of understanding the complex interplay among different modalities. Exploring the landscape of emotional recognition technologies, we encounter several challenges across different models. Analyzing complex emotional data with SVM is difficult due to its sensitivity to kernel, parameter choices, and computational demands. Likewise, the VC(LR-SGD) faces hurdles, including hyperparameter sensitivity, potential biases in unbalanced datasets, and the presupposition that all factors contribute equally. The GBM also grapples with issues like overfitting, handling unbalanced datasets, and significant computational expenses. In conclusion, while each model offers potential for emotional recognition, their limitations underscore the need for careful consideration in model selection and the ongoing pursuit of more adaptable and efficient solutions. This research introduces the Cumulative Attribute- Weighted Graph Neural Network CA-WGNN model, an innovative breakthrough in Sentiment Recognition. The CA-WGNN's graph-based structure excels in attributing weights to features across modalities, adeptly recognizing inter-modal connections, and assigning relevant weights to each modality's features. Its versatility with different modalities and specific dataset characteristics, like those of IEMOCAP and CMU-MOSI, showcases its capability in universalizing Sentiment Recognition models across various datasets. The CA-WGNN's sophisticated grasp of intricate relationships and effective feature integration marks notable progress in this field. The study also offers a detailed comparison of experimental outcomes in subsequent Tables, discussing the CA-WGNN algorithm's benefits, challenges, and environmental impacts. Table 15 presents the accuracy, precision, recall, and F1-scores from IEMOCAP and CMU-MOSI, confirming the algorithm's proficiency in Sentiment Recognition. Table 16 outlines the key benefits and obstacles in implementing CA-WGNN, along with its environmental effects in real-world applications. These tables collectively illuminate the algorithm's performance and flexibility in various environments, underscoring the importance of multimodal integration in affective computing. This comparison particularly emphasizes CA-WGNN's skill in managing complex, cross-modal interactions, vital to the evolution of affective computing. In the context of emotion detection in the text, particularly when examining datasets such as CMU-MOSI, which encompass textual, audio, and visual data, the Receiver Operating Characteristic (ROC) as shown in the Figure 15 curve emerges as a crucial analytical tool. If we take a closer look at the ROC curve using examples from the text portion of two distinct CMU-MOSI datasets, we can gain insights into the model's capability to differentiate between various emotional states expressed in the data. Figure 15 curve graphically illustrates the trade-off between the true positive rate (sensitivity) and the false positive rate (1 - specificity) at different thresholds. By analyzing the happiness and sadness, present in the text data. The area under the ROC curve, we can evaluate how effectively the model distinguishes between different emotional states, such as happiness and sadness, present in the text data. The area under the ROC curve Area Under the Curve (AUC) offers a concise measure of the model's overall performance, with a higher AUC indicating greater accuracy.