## 4 Experimental Design and Result Analysis
### Performance Indicators  
The performance indicators of precision (\(P\)), recall (\(R\)), and \(F1\) value are used to evaluate the sentiment analysis model; these performance indicators range from 0 to 1, and the closer to 1, the better the performance of the model. Precision (\(P\)) represents the ratio of true correct samples in all the data predicted as correct samples; recall @represents the ratio of predicted correct samples in actual correct samples. \(F1\) value trades off precision versus recall [23], which can comprehensively reflect the performance of the model. However, the \(F1\) value is only applicable to the scenario with binary classification task, Macro \(F1\), Micro \(F1\), and Weighted \(F1\) are often used as performance indicators for multi-classification tasks. In this experiment, the \(F1\) value is calculated by weighted method, the Weighted \(F1\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline require\_improvement & 1000 \\ sequence\_length & 64 \\ epochs & 30 \\ learning\_rate & \(4\times 10^{-5}\) \\ batch\_size & 128 \\ dropout & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Public parameter settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Parameters** \\ \hline TextCNN & filter\_sizes = (2, 3, 4), num\_filters = 256 \\ \hline RCNN & hidden\_size = 256, num\_layers = 1, pooling = Max \\ \hline BiLSTM & hidden\_size = 128, num\_layers = 2 \\ \hline BiLSTM-Att & hidden\_size = 128, num\_layers = 2 \\ \hline BERT-CNN & hidden\_size = 768, filter\_sizes = (2, 3, 4), num\_filters = 256 \\ \hline BERT-BiLSTM-Att & hidden\_size = 768, filter\_sizes = (2, 3, 4), num\_filters = 256, \\  & rnn\_hidden = 768, num\_layers = 2 \\ \hline ERNIE-CNN & hidden\_size = 768, filter\_sizes = (2, 3, 4), num\_filters = 256 \\ \hline ERNIE-RCNN & hidden\_size = 768, filter\_sizes = (2, 3, 4), num\_filters = 256, \\  & rnn\_hidden = 256, num\_layers = 2, pooling=Max \\ \hline ERNIE-BiLSTM & hidden\_size = 768, filter\_sizes = (2, 3, 4), num\_filters = 256, \\  & rnn\_hidden = 768, num\_layers = 2 \\ \hline ERNIE-BiLSTM-Att & hidden\_size = 768, filter\_sizes = (2, 3, 4), num\_filters = 256, \\  & rnn\_hidden = 768, num\_layers = 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Special parameter settings.  
value solves the problem that Macro \(F1\) does not consider the imbalance of samples, and the calculation formula is shown in Equation (18):  
\[\begin{array}{llll}F1_{i}=\frac{2\times P_{i}\times R_{i}}{P_{i}+R_{i}}&P_{i}= \frac{Tp}{TP+FR}&R_{i}=\frac{Tp}{TP+FN}\\ P_{w}=\sum\limits_{i=1}^{N}c_{i}\times P_{i}&R_{w}=\sum\limits_{i=1}^{N}c_{i} \times R_{i}&F1_{w}=\sum\limits_{i=1}^{N}c_{i}\times F1_{i}\end{array} \tag{18}\]  
Here:  
\(TP\) is True positive;  
\(FP\) is False positive;  
\(TN\) is True negative;  
\(FN\) is False negative;  
\(P_{i}\) is the precision (\(P\)) of the \(i\)-th classification;  
\(R_{i}\) is the recall(\(R\)) of the \(i\)-th classification;  
\(F1_{i}\) is the \(F1\) value of the \(i\)-th classification;  
\(c_{i}\) is the proportion of the \(i\)-th classification sample to the total sample;  
\(N\) is the total number of classifications. In this experiment, \(N=3\).