# Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification
###### Abstract  
Aspect-based sentiment classification aims at determining the corresponding sentiment of a particular aspect. Many sophisticated approaches, such as attention mechanisms and Graph Convolutional Networks, have been widely used to address this challenge. However, most of the previous methods have not well analyzed the role of words and long-distance dependencies, and the interaction between context and aspect terms is not well realized, which greatly limits the effectiveness of the model. In this paper, we propose an effective and novel method using attention mechanism and graph convolutional network (ATGCN). Firstly, we make full use of multi-head attention and point-wise convolution transformation to obtain the hidden state. Secondly, we introduce position coding in the model, and use Graph Convolutional Networks to obtain syntactic information and long-distance dependencies. Finally, the interaction between context and aspect terms is further realized by bidirectional attention. Experiments on three benchmarking collections indicate the effectiveness of ATGCN. natural language processing; attention mechanism; aspect-based sentiment classification; graph convolutional network +
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this model used two LSTM networks to generate aspect-based sentiment classification results, but it cannot obtain important clues of the aspect. [PERSON] et al. [5] studied features within and between sentences of the input context to distinguish the sentiment polarity. This model is mainly aimed at the situation where a sentence contains one aspect and it is difficult to recognize the sentiment polarity of sentences with many aspects. However, most neural network models are the same as those mentioned above. They are still immature when dealing with the task of text sentiment classification. Recently, in order to better portray different contextual information and reflect the influence of different words in sentences on sentiment polarity, some scholars have introduced the attention mechanism into ABSC tasks. The attention mechanism can well solve the problem of determining the sentiment polarity of each aspect in the sentence. It was originally used for image processing to enable neural networks to focus on certain information when processing data. [PERSON] et al. [6] used attention mechanism in image classification tasks, which verified the effectiveness of the attention mechanism in the image field. The neural network model combined with the attention network has become a major research focus. At present, the attention mechanism has been triumphantly applied in the fields of machine translation [7], reading comprehension [8] and question answering [9]. It is used to strengthen the model's attention to context words that are more closely related to the target semantics. In recent studies, it is common to create aspect precise sentence representations by using attention or transforming sentence representations based on aspect term. In addition, when people are asked to complete the task of ABSC, they will selectively focus on part of the content and the place where the information needs to be established to remember terms. Inspired by this, attention mechanism has been successfully applied, and introduced to the application of neural network structure of ABSC task. For example, [PERSON] el al. [10] designed a model that uses two attention mechanisms to interactively learn aspects and contextual representations. Hence, attention mechanism is indispensable in ABSC task. However, complex recursive neural networks are the main dependence of these studies, acting as sequence encoders to calculate the hidden semantics of the context. Although the research has made great progress, the results can still be improved. The neural network model for context modeling relies on recurrent neural networks, such as LSTM. LSTM has strong expressive power, but it is difficult to parallelize.