# Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
## 2 Literature Review  
At present, researchers in text sentiment analysis mostly use three types of methods: (1) the method of using the sentiment polarity dictionary for statistics; (2) the method of building a machine learning framework; (3) the method of deep learning based on a hierarchical model. However, the sentiment dictionary approach requires researchers to define the judgment rules, and the model cannot break away from these fixed sentiment word restrictions. Moreover, the training of machine learning sentiment analysis is a long task, which is too dependent on the categories marked by the text in the training set. In general, both the sentiment dictionary and machine learning approaches have existing problems, so the deep learning methods came into being. Among them, common deep learning models include CNN, RNN, and Long-Short Term Memory (LSTM), which are often used in the field of sentiment analysis and have made certain contributions. In related research about deep learning methods, [PERSON] et al. [1] put forward the concept of the deep network model; this model uses a layer-by-layer greedy algorithm to overcome the problems of the deep network, so the performance of deep learning in all aspects is effectively improved. [PERSON] et al. [2] proved that convolutional neural networks can extract local n-gram features from text, have strong learning power for local features, perform well in feature extraction and text classification, and are a method that can relatively reduce the amount of calculation. However, a traditional CNN cannot deeply learn pooled features, and has the following shortcomings in feature extraction:  
* The use of sigmoid causes the problem of gradient disappearance and slow convergence [3]. * The deeper the learning layer, the more serious the overfitting problem may be [4]. * The adoption of a gradient descent strategy may lead to an increase in cumulative error [5; 6]. Therefore, this study used a hybrid model based on CNN and BiGRU (CNN + BiGRU) for feature learning. In order to reduce the impact of the vanishing gradient problem in CNN, scholars have designed Gated Recurrent Unit (GRU) to alleviate it. GRU introduces an update gate and a reset gate. The update gate is used to control the extent to which the state information from the previous moment is brought into the current state, and the reset gate controls the amount of information from the previous state that is written to the current candidate set. Both of these gates alleviate the problems of vanishing gradient and long dependence [7]. However, there are some problems in standard GRU, such as incomplete learning of the eigenmatrix and inconsistent influence of the beginning and end of a sentence sequence on the state of the one-way hidden layer [8]. For this reason, the BiGRU network was proposed by scholars. BiGRU is a neural network model jointly determined by the states of two unidirectional and opposite GRUs. At each moment, the input provides two GRUs in opposite directions simultaneously, and the output is determined jointly by the two unidirectional GRUs. For Chinese text, [PERSON] et al. [9] proposed the use of the CNN and BiGRU joint network model to learn text features and extract the feature representation of sentences, thereby improving the accuracy of text sentiment analysis and the calculation speed of the model. [PERSON] et al. [10] constructed a neural network model based on BiGRU which used BiGRU to extract features from deep information of text, and proved through experiments that the model had better accuracy and lower loss rate compared with classical models. [PERSON] et al. [11] proposed a model based on BiGRU and an attention mechanism for the prediction of the novel coronavirus epidemic, and conducted an experiment to prove that BiGRU could reduce the computational cost and make full use of two-way data. In terms of fine-grained text sentiment analysis, [PERSON] et al.