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Abstract
Multimodal Sentiment Analysis (MSA) seeks to interpret human emotions by integrating textual, auditory, and visual data. Leveraging transformer-based architectures, this study introduces a novel framework that effectively fuses these modalities to enhance sentiment classification accuracy. The proposed model employs advanced fusion techniques and attention mechanisms to capture intricate inter-modal relationships. Evaluated on benchmark datasets such as CMU-MOSI and CMU-MOSEI, the model demonstrates superior performance compared to existing state-of-the-art methods, highlighting the efficacy of transformer-based multimodal fusion in sentiment analysis.
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