# Aspect-Level Sentiment Analysis Based on Syntax-Aware and Graph Convolutional Networks
## 4 Experiment
### Analysis of Experimental Results  
As can be seen from Table 4, the results of the CDT model are poorer; the model uses a long short-term memory network to capture the contextual information of the sentence and a graph convolutional network to perform convolutional operations on the syntactic dependency tree to enhance BiLSTM embedding representation. However, focusing on syntactic information with a single-channel GCN ignores the semantic relations of sentences to some extent. In contrast, the RGAT model outperforms the CDT model in the Lappop, Restaurant and Twitter datasets, with 1.02%, 4.30% and 1.49% higher accuracy than CDT, respectively, because the R-GAT model argues that the graph neural network learns sentence-specific aspectual sentiment representations from a dependency tree lacking effective between aspect words and opinion sentiment word dependencies, and that \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 CDT & 77.19 & 72.99 & 82.30 & 74.02 & 74.66 & 73.66 \\ RGAT & 78.21 & 74.07 & 86.60 & **81.35** & 76.15 & 74.88 \\ SentICGCN & 82.12 & 79.05 & 86.92 & 81.03 & - & - \\ DualGCN & 81.80 & 78.10 & 87.13 & 81.16 & 77.40 & 76.02 \\ SSEGCN & 81.01 & 77.96 & 87.31 & 81.09 & 77.81 & 76.08 \\ SAGCN & **83.06** & **79.69** & **87.53** & 81.28 & **77.97** & **76.62** \\ \hline \hline \end{tabular} Bold numbers represent the best experimental results. Symbol “-” indicates that the results of the experiment were not available in the original paper.  
\end{table} 
Table 4: Experimental results. encoding the entire dependency tree introduces noisy information that degrades model performance. Therefore, the model uses a pruned and reconstructed syntactic dependency tree that preserves only edges directly related to aspects and uses aspect words as the root node of the tree in order to focus on the connection between aspects and potential sentiment words. Therefore, the sentiment classification accuracy of the RGAT model increases. Different from the CDT and RGAT models, the SenticGCN model proposes, for the first time, to introduce external sentiment knowledge into the aspect-level sentiment analysis task by augmenting the ordinary dependency graph with external sentiment knowledge and generating a sentiment-enhanced dependency graph, which is then passed into the GCN model to facilitate the model to extract the sentiment dependencies between context words and specific aspects, resulting in a further improvement of the model effect. The DualGCN model also pays attention to syntactic information while considering syntactic structure in order to make the syntactic and semantic information complement each other and proposes an orthogonal regularizer to constrain the attention scores in the model for the purpose of accurately capturing the semantic correlation between words, and the differential regularizer is used to supplement the semantic information that has not been obtained by the model so that the model effect is much higher than that of the CDT model. In order to make full use of syntactic information and to directly link aspect words with sentiment opinion words, the SSEGCN model proposes to use different syntactic distances between words to obtain syntactic mask matrices and learn the structural information from local to global in the sentence through the syntactic mask matrices in order to augment the traditional GCN. Although it achieves good results in the Restaurant and Twitter dataset, it does not fully utilize the information of specific aspects in the sentence.