Abstract: In the aspect-level sentiment analysis task, the attention mechanism is commonly used to obtain the weight information of words, ignoring the effect of syntactic structure on extracting the importance of different words in sentences. In addition, there is a problem of confusion between aspect words and sentiment words in multi-aspect word sentences. How to effectively pay attention to the context part related to the emotional polarity of the target aspect words is often ignored. A graph attention neural network model (GW-GAT) that integrates graph walk information is proposed. Graph walk is performed on the grammar graph to obtain the word weight coefficient of the sentence. The graph attention network is used to combine the word node weight and the weight between the nodes to highlight the context part that plays an important role in the word sen-timent polarity in the target aspect. Finally, the sentiment polarity is obtained through fully connected and softmax layers. The experimental results on the SemEval2014 task and the Twitter dataset show that the GW-GAT model outperforms the baseline model and obtains better experimental results. 

Key words: sentiment analysis, syntax graph, graph walk, graph neural network