# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 4 Experimental Results  
In this study, the experimental framework was meticulously designed to accommodate the rigors of machine learning applications, leveraging a harmonious blend of hardware and software configurations. Central to this framework was a Windows 10 operating system, chosen for its robustness and compatibility with advanced computational tasks. The experimental environment encompassed a dual-platform approach, utilizing both Google Colab and Jupyter Notebook. This methodology facilitated a comprehensive evaluation, allowing for a detailed analysis of various challenges, as enumerated in the accompanying Table 2. A notable aspect of this setup was the dependency of Google Colab's time efficiency on internet connectivity and hardware specifications. From a hardware perspective, the experiments were conducted on a system equipped with a Windows 10 64-bit operating system, complemented by 200 GB of storage in the C drive and 8 GB of RAM. This configuration ensured a seamless operational flow during the testing phases. Python, executed via Jupyter Notebook, was the primary programming language employed, underscoring its versatility and efficiency in handling complex datasets. Integral to our research was the application of GNNs for sentiment analysis, targeting multimodal data encompassing text, audio, and video. This innovative approach, utilizing GNNs, was pivotal in analyzing the interconnected elements within these data types, thereby enhancing the accuracy and depth of sentiment analysis. The incorporation of GNNs allowed for a more nuanced understanding of the sentiment conveyed across different modalities, significantly augmenting the scope of our study in the domain of TER. The CA-WGNN method is introduced for emotion recognition, leveraging trimodal inputs, including text, audio, and video modalities. This approach aims to enhance the interaction among emotional inputs, thereby fostering a more sophisticated and efficient system. The CA-WGNN is evaluated against established methodologies, such as the Support Vector Machine (SVM) [47], the Voting Classifier (integrating Logistic Regression and Stochastic Gradient Descent, denoted as the Voting Classifier, Logistic Regression and Stochastic Gradient Descent (VC(LR-SGD))) [47], and the Gradient Boosting Model (GBM) [47]. Metrics, including accuracy, precision, recall, and the F1-score, are employed to determine the efficacy of inaccurate emotion categorization models. These metrics are essential for assessing the models' robustness across various applications, confirming their reliability and capability to discern complex emotional nuances. In their comprehensive study, [PERSON] et al. [47] conducted a thorough evaluation of the Support Vector Machine (SVM) alongside six other prominent machine learning algorithms: Logistic Regression, Stochastic Gradient Descent, Naive Bayes, Decision Tree, Random Forest, and Voting Classifier, in recognizing emotions through textual tweet classification. This investigation focused on assessing and comparing the performance metrics of these models. Notably, a linear kernel function was employed for the SVM, indicating that the decision boundary is linear. The transformation of tweets into numerical vectors was facilitated through the Term Frequency-Inverse Document Frequency (TF-IDF) vectorization method, which emphasizes the significance and frequency of each word within the tweets. The study adhered to the default parameters for various SVM configurations, including the regularization parameter 'C', which balances the margin width and classification error, the kernel coefficient 'gamma', which affects the influence of individual training samples, the tolerance for the stopping criterion 'tol', and the maximum number of iterations'maxiter.' The research findings demonstrated that SVM was significantly effective, achieving an accuracy of 77% and an F1-score of 79% in a binary dataset that categorized tweets as happy or unhappy.