Multimodal Sentiment Analysis Using TriFusion Attention Network

Multimodal sentiment analysis, which uses data from several modalities like text, audio, and video, has emerged as crucial. It is difficult to combine these many modalities while dealing with noise and redundancy, both of which can impact performance. We propose TriFAN (Trimodal Fusion Attention Network), a cutting-edge method intended to improve multi-modal sentiment analysis, as a solution to these problems. TriFAN has a two-channel architecture: the first channel captures complex cross-modal interactions using attention mechanisms, and the second channel concentrates on removing unnecessary visual and audio elements to leave only the most significant and distinctive information. Our method achieves a 95.6% accuracy rate on the CMU-MOSI dataset, demonstrating its capacity to merge multi-modal data and improve sentiment prediction.