# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 4 Experimental Results
### Accuracy Analysis  
Among the multiple factors considered in emotion detection, accuracy stands as a paramount measure, particularly when comparing the performance of models across multimodal datasets like IEMOCAP Table 3 and CMU-MOSI Table 4. This comparative study focuses on the accuracy in recognizing emotions through text, audio, and video modalities. It delves into the model's proficiency in identifying and classifying emotional expressions accurately across these varied channels. The essence of this analysis is to gauge how effectively the trimodal method captures and aligns with the diverse emotional nuances present in both datasets. Such an evaluation provides pivotal insights into the model's overall capability to decipher emotions, whether conveyed through textual, auditory, or visual means. The results presented in the following sections compare the accuracy of the IEMOCAP and CMU-MOSI datasets, based on the criteria of Sentiment Recognition. The analysis revealed a marginally higher accuracy in the CMU-MOSI dataset (0.94\(\%\)) compared to IEMOCAP (0.93\(\%\)), indicating nuanced differences in the performance of these models in emotion detection across the three modalities. This comparison not only highlights the strengths and limitations of each dataset but also underscores the importance of multimodal approaches in enhancing the accuracy of sentiment recognition systems. The provided table presents a comprehensive overview of the performance metrics for the CMU-MOSI dataset, categorized into three modalities: Text, Video, and Audio. Each \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Category** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Support (\%)** \\ \hline \multirow{3}{*}{CMU-MOSI Text} & Negative & 0.88 & 0.94 & 0.91 & 69 \\  & Neutral & 0.97 & 0.90 & 0.93 & 68 \\  & Positive & 0.90 & 0.90 & 0.90 & 63 \\ \hline \multirow{3}{*}{CMU-MOSI Audio} & Negative & 0.90 & 0.93 & 0.91 & 69 \\  & Neutral & 0.98 & 0.94 & 0.96 & 68 \\  & Positive & 0.91 & 0.92 & 0.91 & 63 \\ \hline \multirow{3}{*}{CMU-MOSI Video} & Negative & 0.88 & 0.96 & 0.92 & 69 \\  & Neutral & 1.00 & 0.94 & 0.97 & 68 \\  & Positive & 0.92 & 0.89 & 0.90 & 63 \\ \hline \multicolumn{3}{l}{**Fusion Overall Accuracy**} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} \\ \hline
**Fusion Macro Average** & 0.94 & 0.94 & 0.94 & 200 \\
**Fusion Weighted Average** & 0.94 & 0.94 & 0.94 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance Metrics for CMU-MOSI Datasets. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Dataset** & **Category** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Support (\%)** \\ \hline \multirow{3}{*}{IEMOCAP Text} & Negative & 0.91 & 0.90 & 0.91 & 69 \\  & Neutral & 0.96 & 0.96 & 0.96 & 68 \\  & Positive & 0.92 & 0.94 & 0.93 & 63 \\ \hline \multirow{3}{*}{IEMOCAP Audio} & Negative & 0.91 & 0.91 & 0.91 & 69 \\  & Neutral & 0.94 & 0.96 & 0.95 & 68 \\  & Positive & 0.94 & 0.92 & 0.93 & 63 \\ \hline \multirow{3}{*}{IEMOCAP Video} & Negative & 0.89 & 0.93 & 0.91 & 69 \\  & Neutral & 0.96 & 0.94 & 0.95 & 68 \\  & Positive & 0.93 & 0.90 & 0.92 & 63 \\ \hline \multicolumn{3}{l}{**Fusion Overall Accuracy**} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} \\ \hline
**Fusion Macro Average** & 0.93 & 0.92 & 0.93 & 200 \\
**Fusion Weighted Average** & 0.93 & 0.93 & 0.93 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance Metrics for IEMOCAP Datasetmodality is further subdivided into Negative, Neutral, and Positive sentiment categories. The table displays essential evaluation metrics, including Precision, Recall, and F1-Score, which are crucial for assessing the effectiveness of a sentiment classification model. Notably, the overall accuracy of the model across all modalities is 94%, based on a total of 200 data points (Figure 7).