Multimodal sentiment analysis based on feature interaction and gate mechanism integration

Solely relying on textual data for sentiment analysis often fails to fully capture users' emotional information, limiting the accurate understanding of audience true feelings and comprehensive evaluations. Various modal information in multimodal data contains richer emotional cues, aiding in a more precise comprehension of audience reactions across multiple sensory dimensions. To better utilize multimodal data, this paper proposes a sentiment analysis approach integrating feature interaction and gate mechanisms. Initially, textual and acoustic features are interacted to capture dependencies through a neural network model. Subsequently, acknowledging the varying importance of text and audio in multimodal fusion, cross-modal interaction and gate mechanisms dynamically adjust weights during fusion. Finally, integrating interaction features, cross-modal attention mechanisms, and gate mechanisms enhances sentiment analysis capabilities. The model is tested on the CMU-MOSI dataset and shows superior performance compared to most advanced methods, significantly enhancing sentiment analysis results.