Enhancing Multimodal Emotion Analysis through Fusion with EMT Model Based on BBFN

Sentiment analysis, as one of the key technologies of natural language processing, has been widely used in medical, film and television fields. In order to increase sentiment analysis's precision, it is particularly important to integrate multi-modal data. This paper presents a pioneering fusion strategy that amalgamates the cutting-edge Efficient Multimodal Transformer (EMT) model with the innovative Bi-Bimodal Fusion Network (BBFN) to revolutionize emotion analysis. By synergistically integrating these two state-of-the-art models, the research endeavors to enhance the efficiency and precision of sentiment analysis in multimodal datasets by accentuating the intricate interplay of global-local cross-modal interactions. Through a rigorous process of meticulous experimentation and comprehensive analysis conducted on the challenging MOSI dataset, the integrated model unveils a plethora of groundbreaking advancements across pivotal metrics, including accuracy, correlation coefficient, and Mean Absolute Error (MAE). The innovative integration surpasses existing models and sets a new paradigm for multimodal sentiment analysis frameworks, highlighting the importance of holistic modal fusion in understanding human emotions.