## 3 Materials and Methods
### Methods
#### 3.3.1 Analysis of User Sentiments and Concerns Information  
(1) Classification of user sentiments on public opinion  
The Bert-BiLSTM model, which possesses both Bert's semantic representation capabilities and BiLSTM's ability to capture sequential dependencies, was selected for sentiment analysis. The model effectively extracts sentiment patterns from public opinion texts, precisely analyzing user sentiments in social media contexts [47]. The process is structured as shown in Figure 3:  
(i) Semantic Encoding: The input text is first encoded into high-dimensional word vectors using a pre-trained Bert model, leveraging its contextualized embeddings to capture rich semantic information. The calculation formula is shown below:  
\[a_{i}=g_{1}(W_{a}\cdot C_{i}+b_{a}) \tag{1}\]  
where \(1\leq i\leq n\), in which n is the dimension of the sentence feature vector output by BERT; \(a_{i}\in\mathbf{R}^{d_{a}}\) is the transformed vector; \(b_{a}\) is the bias vector; \(C_{i}\) is the output of BERT; \(W_{a}\) is the weight; and the activation function \(g_{1}\) uses the Sigmoid function. (ii) Sequential Processing: The encoded sequences are fed into a BiLSTM layer, which captures bidirectional contextual dependencies. A Dropout layer is applied to the concatenated hidden states to mitigate overfitting and improve generalization. The calculation formula is shown below:  
\[v_{i}=\overrightarrow{h}_{i}+\overleftarrow{h}_{i} \tag{2}\]  
where \(\overrightarrow{h}_{i}^{\prime}\in\mathbf{R}^{d_{h}}\), \(\overleftarrow{h}_{i}\in\mathbf{R}^{d_{h}}\), respectively, represent the forward and backward hidden layer outputs at time \(i\), and \(v_{i}\) is the output vector of the hidden layer at time \(i\). (iii) Classification: The output vectors are passed through a fully connected layer, where the SoftMax function converts logits into probability distributions, enabling binary sentiment classification (positive/negative). \[p(y\mid H,W_{s},b_{s})=\text{softmax}(W_{s}\cdot H+b_{s}) \tag{3}\]  
Figure 3: The Bert-BiLSTM sentiments analysis model. where the weight \(W_{s}\in R^{|s|\times|l|}\) and \(b_{s}\in R^{|l|}\) are parameters of the output layer; \(|\;l\;|\) is the number of categories; and \(H\) is the sentence-level feature vector. The classification results are measured using three indicators: accuracy, recall, and F1-score. \[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[\text{Recall}=\frac{TP}{TP+TN} \tag{5}\]  
\[\text{F1-Score}=\frac{2\times TP}{2\times TP+FP+FN} \tag{6}\]  
In the above equation, TP represents the number of positive samples classified as positive, TN represents the number of negative samples classified as negative, FP represents the number of negative samples classified as positive, and FN represents the number of positive samples classified as negative. (2) Extraction of information on users' concerns  
Considering our existing computer resource conditions, we employ a combination of the Latent Dirichlet Allocation (LDA) model, co-occurrence network analysis, and the PageRank algorithm to extract information on user concerns. First, we use the LDA model to extract text topics and determine their distribution through iterative optimization. Then, we represent the opinion text data as a word-frequency matrix and construct an online opinion keyword network via co-occurrence network analysis. Finally, by integrating the PageRank algorithm with node centrality calculation, we obtain the User Concern Index (UCI) of each node. The nodes are sorted in descending UCI order, and the TopN + 1 keyword nodes under each topic are selected as the information on user concern.