## 3 DCCMM Microblog Sentiment Analysis Model  
As shown in Figure 1, the DCCMM model consists of the dual-stream dynamic coding layer, the convolution layer, the cross-channel feature fusion layer, the multi-head self-attention pooling layer, the multi-granularity feature interaction fusion layer, and the Softmax classification and output layer. (1) Dual-Stream Dynamic Coding Layer: We propose a dual-stream dynamic encoding method for characters and words to deal with the frequent occurrence of polysemy and the limited information contained in microblog remarks, and to solve the problems of  
Figure 1: DCCMM model diagram. inaccurate text encoding and incomplete information. WOBERT Plus and ALBERT are used to implement word-level dynamic embedding and character-level dynamic embedding of text, respectively, so that the model can obtain more comprehensive and accurate semantic information. This encoding method is more suitable for sentiment analysis of microblog speeches than the previous methods of static encoding or the dynamic encoding of only a single text feature. 2. Convolution Layer: We use multi-scale convolution kernels to perform convolution operations on adjacent text features to deal with the high degree of irregularity in microblog data. Multi-scale convolution kernels can extract local key information with different granularities and improve the model's ability to perceive emotional polarity. 3. Cross-Channel Feature Fusion Layer: We propose a cross-channel feature fusion mechanism to ensure the integrity of the textual information extracted by the convolutional layer, while enhancing the expressiveness of the local features of microblog speeches, so that the subsequent multi-head self-attention pooling layer learns more accurate global semantic information. 4. Multi-Head Self-Attention Pooling Layer: We propose a new pooling method, multi-head self-attentive pooling, to cope with the limited information contained in microblog text and its requirement for a high level of semantic understanding in the model, and to solve the problem of the loss of key features inherent to most existing model pooling layers. The previous operations of the multi-head self-attention pooling layer in our model only obtain the local information of the text, and do not extract global dependency information. In addition, both average-pooling and max-pooling suffer from the loss of key information, which is not conducive to the acquisition of sentiment information from the microblog text. Our proposed multi-head self-attention pooling mechanism achieves feature optimization and parameter reduction while extracting the global features of the text, and can adaptively extract key features that affect the classification results of the model, fully considering the contribution of each feature to the final results. 5. Multi-Granularity Feature Interaction Fusion Layer: We propose a multi-granularity feature interaction fusion mechanism to cope with the short length and limited content of microblog data, and to solve the problem whereby it is difficult for existing models to effectively utilize and fuse character and word information in the text. In this model, the information granularity of the extracted features varies among channels, and it is not desirable to use simple summation or vector splicing. The multi-granularity feature interaction fusion mechanism can enhance the interactivity between character and word information, realize the complementarity between character-level and word-level information, and strengthen the proportion of important information, while filtering out noisy information, thus resulting in the output of higher quality joint character-word feature information, in order to improve the model's ability to perceive emotional polarity and better cope with the limited amount of information characteristic of microblog text. 6.