## 2 Related Works
### Multimodal Sentiment Analysis Methods  
Multimodal sentiment analysis mainly focuses on using multiple resources to predict human emotions [8]. The main challenge lies in modality fusion. Unlike traditional methods that only use a single modality, multimodal sentiment recognition aims to combine information from multiple sources [9] to improve the understanding and perception of human emotions. Previous multimodal approaches help to exploit complementary information across modalities. For example, [PERSON] et al. [10] propose tensor fusion to explicitly capture interactions involving unimodal, bimodal, and trimodal data. [PERSON] [11] proposes a novel framework, MISA, which projects each modality into two distinct subspaces, reducing inter-modal differences and capturing unique features.  
In addition, existing methods can be broadly categorized based on their fusion techniques and modality representations:  
* Tensor outer products or low-rank tensors to fuse features from different modalities: TFN and LMF [12];
* Factorization methods to decompose multimodal representations into different components: MFM [13] and FDMER [14];
* Attention mechanism to capture correlations and weights between different modalities: MISA;
* Canonical correlation analysis to coordinate different modalities into one hyperspace: ICCA [15].  
Similarly, the node classification baselines commonly used in previous models are divided into the following categories according to the basic principles and application scenarios:  
* Exploit the spectral properties of graphs to define graph convolution operations: GCN [16] and GAE [17];
* Exploit the spatial structure of the graph to define graph convolution operations: GAT [18], GIN [19], GTN [20], etc.;
* Handle heterogeneous graph data with many types of nodes and edges: HAN [21].  
For node classification, HAN is the state-of-the-art (SOTA) model. HAN first extracts high-order semantic relations between different types of nodes in heterogeneous graphs according to the meta-path, and then uses the graph attention network to propagate and aggregate information on each meta-path, obtaining the node information expressed on each meta-path. Finally, the meta-path representation is fed into a hierarchical attention network, whose output is used for classification.  
For multimodal sentiment analysis, the SOTA model is FDMER (Disentangled Representation Learning for Multimodal Emotion Recognition). FDMER is a feature decomposition-based method for multimodal emotion recognition. FDMER first extracts public and private features from text, audio, and visual modalities, and then obtains effective multimodal representations. Finally, the multimodal representation is fed into a Canonical Correlation Analysis (CCA) network, whose output is used for prediction. The above methods are based on examples of feature-level fusion and do not make full use of the complementary and correlated information between modalities. In order to improve the complementary and associated information between modalities, some recent works have introduced attention mechanisms to establish associations between modalities. [PERSON] et al. [22] introduce a multimodal transformer to model unaligned language sequences, improving the integration of text and other modalities like images or audio. [PERSON] et al. [23] discuss the use of multimodality in NLP applications and emphasize the need for effective fusion strategies across different data types. [PERSON] et al. [24] further explore memory fusion for multi-view sequential learning, leveraging sequential data across different modalities for improved understanding. [PERSON] et al. [25] present ICON, an interactive conversational memory network, which uses memory mechanisms to detect emotions across modalities in conversational contexts. However, these models can only describe the relationship between different data. The absence of a quantitative measurement will restrict the impact of subsequent interactions between modalities.