## 3 DCCMM Microblog Sentiment Analysis Model
### Multi-Granularity Feature Interaction Fusion Layer
Equation (18) performs the Hadamard product operation on \(\mathbf{Y_{t}}\) and the filtered feature vector \(\mathbf{Y_{W-CLS}}\). The features contained in \(\mathbf{Y_{W-CLS}}\) are assigned positive and negative weights according to their roles in generating the modulation factor vector \(\mathbf{Y_{W-CLS}}\). If the element value in \(\mathbf{Y_{W-CLS}}\) is 0, this means that the feature values in \(\mathbf{Y_{W}}\) do not need to be corrected. If it is positive, it plays an enhancing and complementary role to the feature values in \(\mathbf{Y_{W}}\), and if it is negative, it plays an attenuating role to the feature values in \(\mathbf{Y_{W}}\). Equation (19) adds the moderation factor vector \(\mathbf{Y_{W-CLS}}\) to the feature values contained in the word-level semantic information vector \(\mathbf{Y_{W}}\) bit by bit, eliminates the noisy features in \(\mathbf{Y_{W}}\), and corrects the feature values in the key regions. Using this approach causes \(\mathbf{Z_{W}}\) effectively to fuse the word-level information contained in \(\mathbf{X_{W-CLS}}\) and \(\mathbf{X_{W}}\) in order to achieve word-level semantic feature refinement. (2) Word-Level and Character-Level Semantic Fusion  
Although character-level semantic information does not involve noise caused by improper word separation or poor word list quality, its semantic information is not as rich as word-level information and may present the problem of there being insufficient semantic information; therefore, it is necessary to further process its information. In addition, when performing multi-granularity information fusion, the information with different granularities will have different importance for the sentiment analysis results. Therefore, when using multi-granularity information for sentiment analysis, it is necessary to distinguish the importance of word-level information from that of character-level information for the sentiment analysis of the target sequence and enhance the weight of important information to obtain more effective joint sentiment features across granularities. \(\mathbf{Z_{W}}\) is the word-level granularity information after refinement, and \(\mathbf{X_{C-CLS}}\) is the character-level granularity information; the two have different information granularity, so the character-level and word-level semantic fusion operation should be adopted differently from the word-level semantic feature refinement to obtain the interaction vector containing both character-level and word-level information. Character-level and word-level semantic fusion is shown in Equations (20)-(24). \[\mathbf{S}=\text{Sigmoid}(\mathbf{W_{3}}\mathbf{X_{C-CLS}}+\mathbf{W_{4}}\mathbf{Z_{W}}+\mathbf{b_{4}}) \tag{20}\]  
\[\mathbf{Y_{C-CLS}}=\text{ReLU}(\mathbf{W_{5}}\mathbf{X_{C-CLS}}+\mathbf{b_{5}}) \tag{21}\]  
\[\mathbf{O_{W}}=\mathbf{Z_{W}}\odot(1-\mathbf{S}) \tag{22}\]  
\[Y_{\mathbf{W}}=\text{ReLU}(\mathbf{W_{1}X_{W}+b_{1}}) \tag{15}\]0  
\[Y_{\mathbf{W}}=\text{ReLU}(\mathbf{W_{1}X_{W}+b_{1}}) \tag{15}\]1  
where \(\mathbf{W_{3}}\), \(\mathbf{W_{4}}\), and \(\mathbf{W_{5}}\) are weight matrices, \(\mathbf{b_{4}}\) and \(\mathbf{b_{5}}\) are bias terms, \(\mathbf{S}\) is the weight vector generated jointly by character-level semantic information \(\mathbf{X_{C-CLS}}\) and word-level semantic information \(\mathbf{Z_{W}}\), \(\mathbf{Y_{C-CLS}}\) is the character-level semantic vector generated by \(\mathbf{X_{C-CLS}}\) after adaptive fine-tuning and feature condensation, \(\odot\) is the Hadamard product, \(\mathbf{O_{W}}\) is the word-level significant feature, \(\mathbf{O_{C}}\) is the character-level significant feature, and \(\mathbf{Z_{C-W}}\) is the interaction vector output by the dynamic fusion mechanism of character-level and word-level semantics. Equation (20) is the weight vector \(\mathbf{S}\) generated by \(\mathbf{Y_{C-CLS}}\) and \(\mathbf{Z_{W}}\) under the action of the Sigmoid function.