# A Multi-Stance Detection Method by Fusing Sentiment Features
## 5 Public Opinion Management  
In this work, a multi-stance indicator system is constructed using the LDA model to mine topics with varying degrees and polarities of stance. Topics are categorized according to the polarity and degree of the stance to which they are related. We employ the coherence \(C_{V}\) to select the right number of topics [29]. Coherence measures the degree of semantic similarity of keywords in the topic [30]. The larger \(C_{V}\) is, the better the model effect. To identify a reasonable number of topics, the experiment iterated the coherence under different topic counts and discovered that the model performed best with 15 topics, as shown in Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(P_{w}\) & \(R_{w}\) & \(F_{w}\) \\ \hline LSTM & 73.75\% & 74.52\% & 71.21\% \\ CNN & 83.04\% & 83.40\% & 83.02\% \\ GRU & 84.12\% & 84.43\% & 84.11\% \\ bi-LSTM-Att & 84.62\% & 84.81\% & 84.69\% \\ TextRCNN & 84.08\% & 84.23\% & 83.59\% \\ BACF & 85.80\% & 85.07\% & 85.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results of verification of generalization. According to the differences that exist in the degree and polarity of the topics in expressing their stances, this paper develops a five-category stance indicator system and classifies the 15 topics into five stances, as shown in Table 9. Word clouds are generated for each stance, and there is a substantial difference in polarity and the degree of stance, as shown in Figure 5.  
Figure 4: Identification of the number of topics. Figure 5: Word clouds for each stance: (**1**) Strong support, (**2**) Weak support, (**3**) Neutral, (**4**) Weak opposition, (**5**) Strong opposition. By analyzing keywords across 15 categories, we can categorize reviews into 15 topics. If a review strongly opposes the behavior of a child and supports the doctor, it is classified as \"SS\". If a review opposes the guardian of the child or online violence or simply considers the child's behavior as too extreme, it is classified as \"WS\". If a review suggests that the doctor is at fault for impulsively hitting the child or believes that the doctor is not at fault for hitting the child, it is labeled as \"WO\". If a review strongly opposes the doctor and suggests legal punishment, it is designated as \"SO\". All other topics fall under the neutral category. Taking \"SS\" as an example, considering keywords such as \"deserved\", \"your child\", \"his family\", \"calm\", and \"powerful\", we can infer the following conclusions from the context of the event and the meaning of these terms. In this case, the phrase \"deserved\" implies that the child's behavior is believed to warrant corresponding consequences. The term \"calm\" may indicate that most people find it difficult to remain rational in this situation. This viewpoint may lead users to be unable to accept the child's behavior and hold a supportive attitude towards the doctor. As can be seen from Table 9, SS believes that the fault lies with the beaten child and backs the doctor's conduct in defending his rights. WS shows solidarity with the doctor by opposing the guardian of the beaten child, etc. SO believes that using violence to uphold rights is utterly unacceptable, while WO believes that the doctor just acted impulsively. In addition, Neutral discusses the details of the event, including the cause and effect of the incident, etc. Then, these issues can be dealt with specifically. For SS, we should pay attention to calming their anger and informing them about the relevant laws; for WS, we need to respond positively to the queries raised by them; for SO, we should promptly inform them of the processing results and meet their demands; and for WO, we must pay attention to