# Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
## 4 Experimental Study, Performance Evaluation, and Comparison
### Comparison and Analysis of Experimental Results  
After many iterations, the CNN + BiGRU model constructed in this article reached its optimal state and the accuracy and loss curve of the model was obtained, as shown in Figure 6 below. It can be seen from observation that as the number of batches increased, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Index** & **Positive Emotion** & **Negative Emotion** \\ \hline accuracy & \(\frac{P1}{P1}\times 100\%\) & \(\frac{N1}{N1}\times 100\%\) \\ recall & \(\frac{P1}{P1}\times 100\%\) & \(\frac{N1}{N1}\times 100\%\) \\ F1 value & \(\frac{2\times N+\frac{N1}{N1}}{\frac{N1}{N1}+\frac{N1}{N1}}\times 100\%\) & \(\frac{2\times N+\frac{N1}{N1}}{\frac{N1}{N1}+\frac{N1}{N1}}\times 100\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculation Formula of Experimental Indices. \begin{table}
\begin{tabular}{c c} \hline \hline CPU & Intel Core i5-8250U \\ RAM & 32G \\ video card & NVIDIA 1070 \\ operation platform & Window 10 \\ Python & Python3.8 \\ Deep learning framework & TensorFlow \\  & jieba = 0.42.1 \\ numpy = 1.18.5 \\  & pandas = 1.0.1 \\ Python tools library & matplotlib = 3.1.3 \\ scikit-learn = 0.23.2 \\  & gensim = 3.8.3 \\ tensorflow = 2.3.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental Environment and Configuration. \begin{table}
\begin{tabular}{c c c} \hline \hline  & epochs: int = 2 & number of network training rounds \\  & batch\_size: int = 64 & network training batch size \\ Training parameters & train\_size: float = 0.9 & training set splits the size \\  & test\_size: float = 0.1 & test set split size \\  & data\_nums\_min: int = 0 & sample dataset size \\ \hline \hline \multirow{4}{*}{Mesh parameter} & maxlen: int = 50 & the maximum length of the entered \\  & vocab\_size: int = 10,000 & sentence \\  & embedding\_dim: int = 100 & size of the word set \\  & embedding\_matrix: int = None & embedded layer dimension, general \\  & dropout\_rate: float = 0.3 & size: \(n\) > 8.33\(\ast\)log (\(N\)) \\  & class\_num: int = 2 & embedded layer pre-training \\  & title\_maxlen: int = 57 & dropout rate \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental Model Parameters. the accuracy of the model continued to rise, the loss continued to drop, the learning rate was suitable, and there was no overfitting situation where loss and acc decreased simultaneously, indicating that the application effect of the model was relatively good. Another important point is that the experiment found that the CNN + BiGRU model has a relatively simple structure and the network training batch size was 64, which will not cause memory explosion and may reduce the training difficulty. Therefore, the CNN + BiGRU model reduced the calculation time and improved the operation efficiency. At the same time, in order to prove the superior reliability of the CNN + BiGRU model described in this paper, it was necessary to show that this model is superior to other methods. Therefore, this study included a comparative experiment in which the CNN + BiGRU model was compared with the traditional CNN model, LSTM model, and C-LSTM model. All experimental models can be simply divided into three parts, namely input, processing, and output. The experimental environment, experimental parameters, and number of iterations were all the same. This ensured that the internal structure of the model was unique and made the comparison results more convincing. The experimental results are shown in Table 7 below. Among all the models, the CNN model had the worst performance in accuracy, recall, and F1 value. The reason may be that, on one hand, the number of 5G mobile phone reviews was relatively small, but they were all more than 30 words. CNN is limited by its inability to solve the problem of long-distance dependence on the context of the sentence, resulting in the loss of some information and making the performance of the CNN model very poor.