# A Data-Driven Approach for University Public Opinion Analysis and Its Applications
## 4 Results and Discussion
### Further Discussion  
Based on the collected data and the tendency results, we can further analyze the public opinion on the topics of \"universities, doctoral students, delayed graduation and thesis publication\". Figure 8 illustrates the tendency distribution of positive and negative public opinions on various social media platforms. Although the general trend is negative, the distribution tendency varies depending on the due to the difference in interactivity. For example, Douvin and WeChat are strongly personalized social network, and the identities of users are easy to recognize; thus, sentiment trends in these apps are positive. As a representative platform for short videos, the ratio of positive attitudes as high as 71%, with the following main opinions represented: \"Strict management should be imposed on Ph.d\"; \"There are reasons for both supervisors and students to postpone graduation\"; and \"Study should be the first priority\". Internet users who are concerned about such topics are influenced by scenario-based immersion. Such topics are related to higher education and professionalism, and positive opinions show that users have a high degree of understanding of universities and the social need to take a positive stand. In contrast, negative attitudes of users are more prominent on platforms such as Toutiao, Weibo and Zhihu, wherein interaction is open but identities is hidden. The main opinions expressed on these platforms include: \"Tutors squeeze \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P** & **R** & **\(F_{1}\)** \\ \hline Proposed & 0.782 & 0.745 & 0.767 \\ Binomial logistic regression & 0.484 & 0.406 & 0.459 \\ Random forest & 0.650 & 0.712 & 0.695 \\ SVM & 0.701 & 0.731 & 0.715 \\ Naive Bayes classifier & 0.562 & 0.495 & 0.528 \\ GMMs & 0.576 & 0.547 & 0.561 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of public online analysis models. Figure 7: Model performance on open datasets. students in various ways\"; \"Students are free workers\"; \"Student cadres should not be directly recommended to be postgraduate candidates\", etc. Among the Toutiao, Weibo and Zhihu apps, the highest ratio of negative trends is 77% (Toutiao), possibly as a result of the one-way output nature of these apps. Although Weibo seems to be interactive, information is published in a free market, and the polarization phenomenon is prominent, reflecting that users are influenced by online public opinion, resulting in emotional expressions. Zhihu prominently features negative evaluation attitudes, representing a critical thinking style. Figure 9 shows the extent to which users on Weibo have moved from wait-and-see to action attitudes on this topic. The peak of behavior difference fluctuates with the evolution of public opinion, including original posts, repost, and repost-and-comment behavior. In the investigated dataset, the volume of public opinion peaked in late March, when a participation value of 3264 was generated on Weibo in one day, mainly comprising repost-and-comment behavior, indicating a high participation rate among users on Weibo and the low sensitivity of the topic. After a period of evolution of public opinion, repost-and-comment behavior decreased to a value of 3057 a day before dissipating. Repost-and-comment behavior is the main choice of users on Weibo according to the investigated dataset. Figure 10 shows a keyword cloud (translated from Chinese words) of public opinion data; \"Zhejiang University\", \"examinations\", \"postpone\", \"dissertation\", \"check\", \"knowledge network\", \"university ranking\", \"dissertation writing\" and \"core journals\" are keywords with high relevance, reflecting the focus of public attention. The proposed data-driven approach can effectively classify the sentimental tendencies hidden in social networks.