# A Data-Driven Approach for University Public Opinion Analysis and Its Applications
## 4 Results and Discussion
### Model Performance  
Chinese word segmentation is the input of the public opinion analysis model; thus, the accuracy of Chinese word segmentation directly determines the accuracy of sentiment classification. Figure 6 shows the results of the proposed Chinese word segmentation model on the MSRA and PKU datasets. The \(F_{1}\) value increases as the number of iterations increases, reaching 95.8% and 94.2% on each of the two datasets, respectively.  
Table 8 shows the accuracy, recall and \(F_{1}\) value of the proposed word segmentation model in comparison with the LTP, NLPIR, Thulac and Jieba methods on the MSRA and PKU datasets. Experimental results suggest that the LSTM-CRF model with Word2 Vec pretreatment achieves the best word segmentation effect. The Word2 Vec model can quantitatively describe the similarity between words and establish mapping between Chinese words and word vectors. Based on this relationship, the LSTM unit can effectively capture the dependencies between sequential texts, grasp the abstract features in the text and ultimately map the features to segmentation sites through the CRF layer.  
Table 9 shows the performance of the deep learning model, in which binary logistic regression (BLR), random forest (RF), support vector machine (SVM), naive Bayes classifier (NBC) and Gaussian mixture models (GMM) are used to compare with the proposed method. Because BLR, RF, SVM, NBC and GMM are machine-learning-based approaches, their implementation requires handcrafted features. The effect of machine learning models  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MSRA**} & \multicolumn{3}{c}{**PKU**} \\ \cline{2-7}  & \(P\) & \(R\) & \(F\) & \(P\) & \(R\) & \(F\) \\ \hline Proposed & 0.962 & 0.955 & 0.958 & 0.944 & 0.940 & 0.942 \\ LTP & 0.865 & 0.893 & 0.835 & 0.916 & 0.895 & 0.911 \\ NLPIR & 0.866 & 0.912 & 0.895 & 0.936 & 0.941 & 0.937 \\ Thulac & 0.931 & 0.922 & 0.925 & 0.943 & 0.911 & 0.921 \\ Jieba & 0.815 & 0.811 & 0.819 & 0.853 & 0.799 & 0.821 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison of the Chinese word segmentation model.  
Figure 6: \(F_{1}\) values of the Chinese segmentation model.  
considerably depends on feature selection, and they do not perform well in the experiments. The deep learning model can automatically extract deep semantic features in sentences on the basis of word vectors, and the semantic features are high-dimensional features that can better reflect the implicit information in the opinion corpus. Without complex feature engineering, this end-to-end model achieves excellent classification performance, with accuracy, recall and \(F_{1}\) values of 78.2%, 74.5% and 76.7%, respectively. To further validate the classification performance, the model is evaluated on open datasets, including IMDb, SST-2 and MRPC. As shown in Figure 7, the established deep learning model achieves better results on the standard datasets than the self-built dataset, with classification accuracies of 88.5%, 87.6% and 85.1% achieved on the IMDb, SST-2 and MRCA datasets, respectively, also outperforming popular handcrafted-feature-based machine learning methods, such as SVM and NBC. Based on these results, the proposed deep-learning-based public opinion analysis model is effective in classifying sentimental tendencies hidden in Chinese text content.