<![CDATA[Emotion is a condition that plays an important role in human interaction and is the main focus of intelligence research in utilizing multimodal. Previous studies have classified multimodal emotions but are still less than optimal because they do not consider the complexity of human emotions as a whole. Although using multimodal data, the selection of feature extraction and the merging process are still less relevant to improving accuracy. This study attempts to categorize emotions and improve precision through a multimodal methodology that utilizes Transformer-based Fusion. The data used consists of a synthesis of three modalities: text (extracted through BERT and assessed through the affective dimensions of NRC Valence, Arousal, and Dominance), audio (extracted through MFCC and delta-delta2 from the RAVDESS and TESS datasets), and images (extracted through VGG16 on the FER-2013 dataset). The model is built by mapping each feature into an identical dimensional representation and processed through a Transformer block to simulate the interaction between modalities, known as feature-level interactions. The classification procedure is run through a dense layer with softmax activation. Model evaluation was performed using Stratified K-Fold Cross Validation with k=10. The evaluation results showed that the model achieved 95% accuracy in the ninth fold. This result shows a significant improvement from previous research at the feature level (73.55%), and underlines the effectiveness of the combination of feature extraction and Transformer-based Fusion. This study contributes to the field of emotion-aware systems in informatics, facilitating more adaptive, empathetic, and intelligent interactions between humans and computers in practical applications.]]>
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