Enhanced Multimodal Sentiment Analysis via Integrated Spatial Position Encoding and Fusion Embedding

Multimodal sentiment analysis aims to understand emotions from text, speech, and video data. However, current methods often overlook the dominant role of text and suffer from feature loss during integration. Given the varying importance of each modality across different contexts, a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process. In response to these critical limitations, we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues. In our model, text is treated as the core modality, while speech and video features are selectively incorporated through a unique position-aware fusion process. The spatial position encoding strategy preserves the internal structural information of speech and visual modalities, enabling the model to capture localized intra-modal dependencies that are often overlooked. This design enhances the richness and discriminative power of the fused representation, enabling more accurate and context-aware sentiment prediction. Finally, we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model. The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks.