# Aspect-Level Sentiment Analysis Based on Syntax-Aware and Graph Convolutional Networks
## 5 Conclusions  
In this paper, we propose a syntax-aware and graph convolutional network-based sentiment analysis model for aspect-level sentiment analysis tasks. The model first uses BERT to pre-train the language model to obtain embedded representations of sentences and aspectual vocabulary, and then obtains syntactic knowledge and semantic information of sentences through syntactic-aware and semantic-assisted modules, respectively. In the syntactic-aware module, sentence dependency graphs are obtained through dependency parsing, and then external affective common sense knowledge is introduced into  
Figure 3: Effect of the number of GCN layers on the model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Twitter**} \\ \cline{2-7}  & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** \\ \hline w/o Sentie & 82.12 & 78.51 & 86.02 & 80.11 & 76.37 & 75.12 \\ w/o Aspect & 82.86 & 79.14 & 87.16 & 81.72 & 77.21 & 75.89 \\ w/o Semantic & 81.12 & 77.81 & 85.92 & 79.02 & 75.73 & 74.1 \\ SAGCN & **83.06** & **79.69** & **87.53** & **81.28** & **77.97** & **76.62** \\ \hline \hline \end{tabular} Bold numbers represent the best experimental results.  
\end{table} 
Table 5: The results of the ablation study. dependency graphs, used to augment the dependency graphs and provide more accurate sentiment representations of the different aspects of the sentence. In the semantic auxiliary module, multi-head self-attention and Point-wise Convolutional are used to supplement the semantic information and enrich the sentiment features of specific aspects. In order to highlight the importance of aspect information, specific aspect information is additionally supplemented in both semantic and syntactic modules. The experimental results on three benchmark data demonstrate the effectiveness of the SAGCN model proposed in this paper. Compared to the most basic CDT model consisting of graph convolutional networks, the accuracy of the SAGCN model in this paper is improved by 5.58% in the Laptop data, whereas the accuracy on the Restaurant and Twitter datasets is improved by 5.23% and 3.31%, respectively. The accuracy of classification is greatly improved. Although the SAGCN model achieved good results in the ALSA task, there are still some limitations, and we will further improve our method based on these limitations in future research. Firstly, the model overly relies on dependencies between words and ignores other complex relationships in the sentence, which may lead to a decrease in the accuracy of the model. For this problem we plan to integrate the composition tree into the aspect-level sentiment analysis model, enriching the syntactic information through the composition tree and improving the accuracy of the model's classification. Secondly, the dependence on pre-trained models such as BERT may affect the performance of the model and the repeatability of the experiments. To address this problem, we plan to freeze the weights of the pre-trained models and control the version of the model to minimize the sensitivity of the model to changes in external resources and improve the reliability and repeatability of the experiments. Thirdly, the model architecture is relatively complex, which is not convenient for practical application. For this, we consider pruning or compressing the model to reduce the model size and the number of parameters so as to improve the scalability and efficiency of the model in practical application. Conceptualization, Q.G. and R.W. ; Methodology, Q.G. and Z.W. ; Software, Z.W. ; Validation, Q.G., S.S. and R.W. ; Formal analysis, H.Z. ; Writing--original draft, Z.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Publicly available datasets were analyzed in this study.