###### Abstract  
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that mainly judges the polarity of a given aspect word in a review. Current methods mainly use graph networks to do aspect-level sentiment classification tasks, most of which use syntactic or semantic graphs, and utilize attention mechanisms to interact and correlate aspect terms and contexts to obtain more useful feature representations. However, these methods may ignore some insignificant syntactic structures and some implicit information in some sentences. The attention mechanism then easily loses the original information, which eventually leads to inaccurate sentiment analysis. In order to solve this problem, this paper proposes a model based on residual attention gating and three-channel graph convolutional network (RAG-TCGCN). Firstly, the model uses a three-channel network composed of syntactic information, semantic information, and public information to simultaneously optimize and fuse through the multi-head attention mechanism to solve the problem of sentences without significant syntactic structure and with implicit information. Through the residual attention gating mechanism the problem of loss of original information is solved. Experimental verification shows that the accuracy and F1 value of the model are improved on the three public datasets. residual attention gating mechanism (RAG); three-channel network; graph convolution +
Footnote †: journal: Medical  
information, among which mining the most relevant opinion words is the key. With the successful application of the attention mechanism, some researchers combine the attention mechanism with a neural network to complete various tasks, and have achieved good results. Later, a large body of work used attention mechanism-based neural networks for sentiment analysis [11; 12]. However, the attention mechanism introduces noise, which causes the model to learn context words that are not relevant to the current aspect [13; 14]. For example, in the comment \"The food is delicious but the service was mediocre\", when analyzing the sentiment polarity of \"food\", we want the model to only focus on sentiment words related to \"food\" in \"the food is delicious\", rather than sentiment words unrelated to \"food\" in the entirety of the review text. Recently, the rapid development of graph neural networks has attracted great interest, and a class of graph neural networks has been designed to extract syntactic information from dependency trees due to its enormous ability to learn structural representations [15; 16; 17; 18; 19]. Compared with attention-based models, although these models have made many improvements, their shortcomings cannot be ignored. First, sentences have different sensitivities to grammatical information and semantic information. Those sentences whose syntactic structure is not obvious have an especially low sensitivity to syntactic information, which means that in some cases syntactic information may not help the model to determine the sentence's emotional polarity. In response to the above problems, we propose the RAG-TCGCN model, which uses a three-channel network composed of syntactic information, semantic information, and public information to simultaneously optimize and fuse through a multi-head attention mechanism to solve the problem of sentences without significant syntactic structure and with some implicit information. The attention mechanism can capture words related to the emotional expression of the aspect term, and the essence of the attention mechanism is to increase the weight of some information and weaken others [20]. However, the part weakened by the attention mechanism may lead to the loss of some useful information, and the attention mechanism will bring noise. Therefore, we use the residual attention gating mechanism to solve this problem, which addresses noise through gating and alleviates information loss through a residual mechanism. The main contributions of this paper are as follows:  
1.