Multimodal Sentiment Analysis Based on Heterogeneous Directed Acyclic Graph Neural Networks

In multimodal sentiment analysis, existing methods often struggle with dynamic context modeling in multi-speaker dialogues, speaker interaction, and effective multimodal fusion. To address this, we propose a model based on a heterogeneous directed acyclic graph neural network. It uses BiLSTM and Gated TCN for multi-granularity context modeling and introduces speaker embeddings to capture individual expression differences. The DAG structure helps avoid cyclic dependencies and oversmoothing in traditional GNNs. Experiments show weighted F1 scores of $\mathbf{7 0. 6 8 \%}$ on IEMOCAP and $\mathbf{6 0. 8 3 \%}$ on MELD.