Fusion Techniques and Contextual Integration in Multimodal Sentiment Analysis

Multimodal Sentiment Analysis (MSA) is concerned with the problem of inferring human emotion from blended textual, visual, and auditory signals, and modality fusion being a central bottleneck. This paper overviews the history of MSA fusion methods, ranging from basic averaging to sophisticated deep learning techniques such as cross-modal attention and Large Vision-Language Model integration. We present a new hierarchical sequence-to-sequence model, L2-MCTN, with cyclic consistency constraints to address temporal misalignment and modality incongruence. On the CMU-MOSI dataset, L2-MCTN attains a Mean Absolute Error (MAE) of 0.67 and 80.3% 7-class accuracy, outperforming models such as MulT and TFN with higher parameter efficiency. The work also touches upon current MSA issues, such as heterogeneity of data and computational requirements, and presents future directions toward explainable, efficient, and ethical systems.