# Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
## 3 Construction of CNN + BiGRU
### Bidirectional Gating Recurrent Unit  
The Bidirectional Gating Recurrent Unit Network model has a simpler structure compared with LSTM. Thus, the training of it is more easy. Firstly, the BiGRU model uses gates to suppress the loss of information. The structure of the BiGRU model integrates the output gate and the forget gate in LSTM to form an update gate. Therefore, its structure is streamlined, which can save disk space to a large extent [9]. Secondly, the BiGRU does not control and retain internal memory, which reduces the consumption of memory space. The BiGRU highlights the key information of a text through output in two directions, and assigns corresponding weights to the extracted deep-level features to obtain a better feature extraction effect. The GRU model can automatically learn which resources are useful and which resources can be discarded through training. Therefore, in extended texts, it may perform better. However, in a one-way GRU network, the state is always output from the front to the back and only one-way time series can be processed, which makes it easy to miss information when performing text sentiment analysis. There are also related studies that have proved that using a Bi-GRU bidirectional model performs better than GRU. [PERSON] et al. [29] predicted the time series of new COVID-19 cases and new deaths through LSTM, convolutional LSTM, and GRU, and the results show that the error of the two-way model was lower than that of the one-way structure model. [PERSON] et al. [30] proposed an attention mechanism recognition model based on BiGRU, which was applied to ship fault recognition, and proved through experiments that the time consumption of the bidirectional GRU network was smaller than that of other models, and the accuracy and recall rate were better. Therefore, this study used the two-way GRU model to obtain text vectors in two flow directions. In this model, another layer is added on the basis of a layer of GRU, and the directions of the two layers are different, so that not only the above information can be processed, but the following information can also be processed at the same time and plays an intermediate transition role. The positive and negative features are merged to obtain more complete feature vector information of the text, and the input of the model is optimized. The BiGRU model is composed of two GRUs that are unidirectional and opposite in direction, combined to form a neural network model. All resources flowing through this network model are simultaneously used by the two layers of GRU. BiGRU consists of two layers of GRU: forward output GRU and reverse output GRU. The GRU model includes a reset gate and an update gate. The role of the reset gate is equivalent to that of the forgetting gate and input gate in LSTM, which determines how much the information at the previous moment is related to that of the current one. That is, there are some resources that do not need to be memorized. These meaningless resources are discarded by resetting the gate. The calculation formula is as follows:  
\[R_{t}=\sigma(W_{r}\cdot[h_{t-1},X_{t}]) \tag{1}\]  
where \(R_{t}\) represents the reset gate and \(W_{r}\) is the weight matrix. \(X_{t}\) represents the input at time \(t\) and \(h_{t-1}\) is the output at the previous moment. In this part, the Hadamard operation is calculated to determine which information needs to be discarded and which information is retained. The operation can give a result within the interval {0, 1}. If the value of an element is 0, then it is completely useless; if the score of a value is closer to 1, then it indicates that it is more important. The update gate determines when to update the state of the cell. For example, in the long sentence \"the ipone which..., is red\", in order to correctly generate the word \"is\", it needs to be in the input \"ipone\" to keep its information, so that the model will know that the input is a singular number.