## 2 Related Works
[19] used multihead attention to extract accurate semantic and affective information in the representation fusion stage, followed by training multiple base classifiers to make independent judgments on different unimodal representations in the decision fusion stage, and finally fusing base classifiers' decisions. The core idea of this approach is to allow features to be fused at different stages of the model while avoiding some of the potential problems of early fusion and late fusion. However, the limitations of the baseline model itself at that time made this type of fusion method not perform well enough. Word-level fusion is a method that fuses word representations in the temporal dimension to capture the interrelationships between different modalities. This approach emphasizes word-level information interactions and helps to understand the intrinsic structure and semantic relatedness of multimodal data in more detail. In [20], [PERSON] et al. proposed a Memory Fusion Network (MFN); they first modeled interactions within modalities and generalized temporal relationships across modalities, ultimately unifying sequences based on relationships between unimodal word-level features. Subsequently, in [21], they used a Graph-Memory Fusion Network to perform unimodal, bimodal, and trimodal word-level fusion for unimodal features, and captured intermodal interactions by using a dynamic \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method Type** & **Description** & **Advantages** & **Flaws** \\ \hline \multirow{2}{*}{Early fusion} & Combines all of the features from different modalities into a vector. & Realizes modal interactions at the early stage. & Time asynchrony and information redundancy. \\ \hline \multirow{2}{*}{Late fusion} & Employs independent classifiers separately for each modality. & Helps model to better integrate semantic information. & Usually involves more complex model structures. \\ \hline \multirow{2}{*}{Hybrid fusion} & Combines the advantages of early fusion and late fusion & Balance the model’s complexity. & Inefficiencies arising from the limitations of the backbone network. \\ \hline \multirow{2}{*}{Word-level fusion} & Fuses word representation in the temporal dimension. & Helps model to understand the intrinsic relation of multimodal data. & \multirow{2}{*}{Insufficient generalization.} \\ \hline \multirow{2}{*}{Tensor-based} & Utilizes various tensor-based methods to integrate information from different modalities. & Integrate multimodal data effectively and address the complexity and noise issues. & Excessive computation and lack of interpretability. \\ \hline \multirow{4}{*}{Attention-based} & Learns the semantic and relevant information using different attention mechanisms or Transformer. & More flexible and accurate in processing temporal information and capturing interactions between different modalities. & Correlations between different attention mechanisms cannot be captured. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related works in multimodal sentiment analysis. fusion graph. [PERSON] et al. [22] proposed an LSTM-based model, obtaining cross-modal interactions by performing multiple stage fusion of modalities features between each time step. [PERSON] et al. [23] proposed Recurrent Attended Variation Embedding Network (RAVEN) by modeling the fine-grained structure in word segments and transforming word representations based on nonverbal dynamic information. Word-level fusion enables the integration of affective information from different modalities in word representations. However, this approach may result in the loss of specific affective information in the original modality, and the complexity of word-level fusion increases further when multiple different modalities are involved. Tensor fusion utilizes various tensor-based methods to integrate information from different modalities. These methods can effectively integrate multimodal data and address the complexity and noise issues in the data.