## 2 Related Works  
Multimodal sentiment analysis aims to obtain sentiment information from different types of data. It provides additional sources of information for affective computing and enables computers to understand and perceive human sentiment more accurately [1; 2; 3; 4]. A key challenge in this area is determining how to efficiently fuse data from different modalities so that the model can recognize sentiment precisely. This section presents related works on multimodal sentiment analysis, including early fusion, late fusion, hybrid fusion, word-level fusion, tensor-based fusion, attention-based methods (Table 1 provides a brief description of several of these methods), and other recent research approaches. Early fusion combines all of the features from different modalities (text, audio, and visual) into a single feature vector, which is then used for sentiment prediction using a classification algorithm or model. [PERSON] et al. input three unimodal features into the HMM model simultaneously [9]. [PERSON] et al. proposed a method using CNN networks [10], by feeding unimodal features into a multikernel learning classifier. Following this, [11] proposed an LSTM-based model to deal with different unimodal features and explored the contextual relationships between modalities. [PERSON] et al. [12] concatenated the multimodal features at each time step, used Multi-Attention Block to capture the dynamics between different modalities, and used a Long-short Term Hybrid Memory to store the dynamic information associated with each modality. [PERSON] et al. [13] proposed a Select Additive Learning based on CNN model (SAL-CNN) to improve the generalization performance of the model. The advantages of these approaches are that they can take into account the correlation between different modality features at the early stage. However, premature fusion of unimodal features can prevent the model from capturing information about the dynamics within the modalities, which can affect the model's ability to perform fine-grained classification. In contrast to early fusion, late fusion employs independent classifiers separately for unimodal data and then fuses the outputs of each model to generate the final multimodal representation, or votes on the results of each model. [PERSON] et al. [14] used the Kalman filter as the combiner for temporally ordered classifier decisions. It is a linear dynamical system based on a Markov model which is well suited for real-time classifier fusion. [PERSON] et al. [15] used text CNN, image CNN, and multi CNN to process unimodal featuresand multimodal features, respectively; they used logistic regression as a classifier with the vectorized features in the penultimate layer of different CNNs. In [16], [PERSON] et al. generated their classification models using Sequential Minimal Optimization(SMO, which is a variant of SVM) for each feature set, and different kernel functions were used for different feature sets. Finally, the results of classifiers for different feature sets were fused using decision fusion. While late fusion helped the model to better integrate semantic information. However, the model is not able to obtain the interactions between modalities during the training process, which would prevent the model from capturing cross-modal dynamic information. In addition, it is usually accompanied by a more complex model structure and a larger number of parameters. Hybrid fusion combines the advantages of early fusion and late fusion, capitalizing on their strengths and compensating for their weaknesses, respectively. [PERSON] et al. [17] proposed a method for extracting text features using deep CNNs and fusing multimodal heterogeneous features using MKL, in addition to a decision-level fusion method that determines the weights of the text modalities by the coupling of the sentiment modalities. [PERSON] et al. [18] used gating mechanisms to selectively learn cross-modal interaction information and utilized post-interaction results for sentiment prediction. [PERSON] et al.