## 3 DCCMM Microblog Sentiment Analysis Model
### Multi-Granularity Feature Interaction Fusion Layer  
The information granularity of the CLS tag vector \(\mathbf{X_{W-CLS}}\) output by WOBERT Plus, the CLS tag vector \(\mathbf{X_{C-CLS}}\) output by ALBERT, and the multi-head self-attention pooling layer output vector \(\mathbf{X_{W}}\) are not the same; \(\mathbf{X_{W-CLS}}\) and \(\mathbf{X_{W}}\) belong to the word-level semantic information and \(\mathbf{X_{C-CLS}}\) belongs to the character-level semantic information. Different information granularities have different levels of importance in different contexts and produce different effects in the classification results. In addition, factors such as improper word separation may introduce noise into the word-level text information, and the character-level semantic information may present the problem of containing insufficient semantic information, necessitating further processing of the information. Therefore, it is not reasonable to process character-level and word-level semantic information by means of direct splicing or simple summation. To enhance the interactivity between character- and word-level information, realize the complementarity between character-level and word-level information, and strengthen the weight of important information while filtering out noise, thus resulting in the output of more effective joint character and word feature information, we propose a multi-granularity feature interaction fusion mechanism in this paper. The multi-granularity feature interaction fusion mechanism is shown in Figure 2. The multi-granularity feature interaction fusion mechanism mainly consists of two parts: word-level semantic refinement and word-level and character-level semantic fusion. (1) Word-Level Semantic Refinement  
Word-level semantic feature refinement is used to correct and remove noise from the information to achieve an effective fusion of word-level granularity information \(\mathbf{X_{W-CLS}}\) and \(\mathbf{X_{W}}\). \[Y_{\mathbf{W}}=\text{ReLU}(\mathbf{W_{1}X_{W}+b_{1}}) \tag{15}\]  
\[Y_{\mathbf{W-CLS}}=\text{ReLU}(\mathbf{W_{2}X_{W-CLS}+b_{2}}) \tag{16}\]  
\[Y_{t}{=}\tanh(Y_{\mathbf{W}}+Y_{\mathbf{W-CLS}}+b_{3}) \tag{17}\]  
\[Y_{\mathbf{W-CLS}}^{t}=Y_{\mathbf{W-CLS}}\odot\mathbf{Y_{t}} \tag{18}\]  
\[Z_{\mathbf{W}}=\mathbf{Y}_{\mathbf{W-CLS}}^{t}+\mathbf{Y_{W}} \tag{19}\]  
where \(\mathbf{W_{1}}\), \(\mathbf{W_{2}}\) are weight matrices, \(\mathbf{b_{1}}\), \(\mathbf{b_{2}}\), \(\mathbf{b_{3}}\) are bias terms, and \(\mathbf{Y_{W-CLS}}\) and \(\mathbf{Y_{W}}\) are word-level semantic vectors obtained after feature condensation and noise filtering for \(\mathbf{X_{W-CLS}}\) and \(\mathbf{X_{W}}\), respectively. \(\mathbf{Y_{t}}\) is the weight vector generated by combining \(\mathbf{Y_{W-CLS}}\) and \(\mathbf{Y_{W}}\). \(\mathbf{Y_{W-CLS}}\) is the vector of moderation factors generated using \(\mathbf{Y_{W-CLS}}\) and \(\mathbf{Y_{t}}\). Sign \(\odot\) denotes Hadamard product. \(\mathbf{Z_{W}}\) is the vector after implementing key feature correction for \(\mathbf{Y_{W}}\), which effectively incorporates word-level semantic information. Since the ReLU function can map negative values to zero, Equation (15) uses the ReLU function to generate the vector \(\mathbf{Y_{W}}\) by initially filtering the noisy regions in the word-level vector \(\mathbf{X_{W}}\). Similarly, Equation (16) uses the ReLU function to filter the noisy region in \(\mathbf{X_{W-CLS}}\) to generate \(Y_{\mathbf{W-CLS}}\) so that the modulation factor vector \(\mathbf{Y_{W-CLS}}\) does not contain invalid and disruptive information that could lead to noisy information in the vector \(\mathbf{Z_{W}}\). Figure 2: Multi-granularity feature interaction fusion mechanism diagram. Since the tanh function maps the eigenvalues to the interval \((-1,1)\), the range it can regulate is broader and more flexible compared to that available to the Sigmoid function. Equation (17) generates the weight vector \(\mathbf{Y_{t}}\) using the tanh function for \(\mathbf{Y_{W-CLS}}\) and \(\mathbf{Y_{W}}\).