A Directional Semantic Enhancement Approach with Gated Fusion for Multimodal Arabic Sentiment Analysis

Multimodal Arabic sentiment analysis has gained increasing attention due to the growing volume of user-generated multimedia content. However, effectively integrating textual, acoustic, and visual modalities remains challenging because of modality imbalance and weak cross-modal alignment. This study proposes a Directional Semantic Enhancement approach with Gated Fusion to address these limitations. The objective is to explicitly model similarity-guided semantic transfer between modalities while adaptively regulating information flow during fusion. The proposed architecture consists of four main stages: modality encoding, directional semantic enhancement, gated fusion, and classification. Directional semantic interactions enable structured cross-modal knowledge exchange, while adaptive gating mechanisms balance original and enhanced representations to mitigate modality-specific noise. Extensive experiments are conducted on the Ar-MuSA benchmark dataset, which contains 8700 multimodal samples. The proposed approach achieves 89.89% accuracy and an F1-score of 0.8989 with a latent dimension of 1024, outperforming early fusion, late fusion, and recent state-of-the-art methods. The study highlights the importance of controlled cross-modal alignment and provides a scalable approach for robust multimodal sentiment understanding in Arabic multimedia environments.