# Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism
## 4 Experimental Results and Analysis
### Model Comparison  
The experiment used the accuracy index (_Accuracy_) to measure the performance of the evaluation sentiment classification algorithm. The calculation method is as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}, \tag{28}\]  
where TP indicates the number of samples that are positive and the prediction is positive; FN indicates the number of samples that are positive and the prediction is negative; TN indicates the number of samples that are actually negative and the prediction is negative; FP indicates that the actual sample is negative and the prediction is positive.  
To verify the validity of the model proposed in this paper, the experiment sets up six baseline methods:  
(1) SVM: The traditional feature-based SVM classification model proposed by [PERSON] et al. [34]. It uses a series of manually annotated data to train the model, which yields better classification results than previous studies.  
(2) CNN: The convolutional neural network model based on [PERSON] [5] is the most basic convolutional neural network model.  
(3) AM-CNN: Discussed by [PERSON] et al. [35], it is based on the attention mechanism convolutional neural network. The algorithm uses the cyclic neural network to capture the context information of the text and introduces the single-level attention mechanism to obtain the text category matrix, thereby improving the classification accuracy of the model.  
(4) CNN+BiLSTM: [PERSON] et al. [36] proposed a fusion model based on CNN and BiLSTM. The model organically integrates CNN in the BiLSTM model, and the experiment proves that it has a significant improvement over the single model classification effect.  
(5) ATT-LSTM: An attention-based LSTM network proposed by [PERSON] et al. [37], the model incorporates the attention information of a specific target and uses the pre-trained word2 vec word vector as input to train the model. This model achieves a better classification effect in the field of sentiment classification than the traditional LSTM network.  
(6) BiLSTM-ATT-G [38]: This uses two attention-based LSTMs to model the context information on the left and right sides of the target to extract a distributed representation of the words on the sentence and then applies attention to the hidden nodes. It estimates the importance of each word and introduces a gate function to calculate the left and right sides of the context and the sentimental tendencies of the entire sentence.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Parameter Description** & **Value** \\ \hline h & Windows Size & \(3,4,5\) \\ \hline n & Features Map & 128 \\ \hline p & Dropout Rate & 0.5 \\ \hline s & Constrain L & 3 \\ \hline b & Mini-Batch Size & 64 \\ \hline m & Pooling Method & k-max \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameters.  
(7) IAN [39]: IAN has designed a model for interactive computing of aspect terms and sentences, which leverages attention in context and aspect terms to generate representations of aspect terms and context, respectively. Finally, the sentiment polarity of an aspect term in context is predicted by combining the aspect term and context representation.