## 4 Experiment
### Results and Analysis  
To evaluate our model, we use Accuracy and Macro-F1 score as the main metrics of estimation to be compared with existing baseline models. The principal findings of the experiment are listed in Table 2. Our model shows validity on the Laptop, Restaurant, and Twitter datasets, with Accuracy of 81.80, 87.26, and 78.35, and Macro-F1 score of 78.63, 83.08, and 77.27, respectively. The results show that the model proposed in this paper shows some performance improvement with our method in most cases, compared to other recently published methods. Our proposed model can integrate semantic and syntactic information. For attention-based methods (e.g., IAN and AOA) capable of automatically capturing useful features in a sentence, AOA performs better than IAN because it pairs capture interactions between aspect and context sentences in a joint manner and important parts of the sentence are automatically attended to, and interactions between aspects and contexts are learned through an interactive network. In contrast, our model fuses syntactic structure and semantic information, which improves the information integration ability when dealing with complex sentence structures and avoids the noise that the attention mechanism may introduce in complex contexts. For syntax-based methods (e.g., CDT, ASGCN, and R-GAT), it is more valid than attention-based models (e.g., AOA, IAN) because syntactic information is introduced into  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Twitter**} \\ \cline{2-7}  & **Accu. ** & **Macro-F1** & **Accu. ** & **Macro-F1** & **Accu. ** & **Macro-F1** \\ \hline IAN, 2017 [27] & 78.60 & - & 72.10 & - & - & - \\ AOA, 2018 [28] & 81.20 & - & 74.50 & - & - & - \\ ASGCN, 2019 [33] & 80.77 & 72.02 & 75.55 & 71.05 & 72.15 & 70.40 \\ CDT, 2019 [7] & 82.30 & 74.02 & 77.19 & 72.99 & 74.66 & 73.66  
the model. R-GAT performs a little better than ASGCN and CDT because it will better capture the link between dependencies and aspects and codify the dependency tree using R-GAT. Although syntax-based methods offer a significant performance improvement over attention-based methods, they neglect semantic information between words. There are also methods that utilize the attention mechanism rather than syntactic structures to capture knowledge characteristics, such as dotGCN, which utilizes attention and reinforcement learning to capture sentiment aspects and sentiment polarity pairs, which can achieve good results. Nevertheless, when dealing with complex sentences, using only syntactic structures leads to poor performance. Methods that consider both syntactic and semantic features (e.g., DualGCN, RAG-TCGCN, SSEGCN, SSEGAT, GMF-SKIA, and LOSIT) are able to capture sentence information more comprehensively, which helps the model understand sentiment polarity more accurately. DualGCN performs a little better than RAG-TCGCN and GMF-SKIA because it utilizes a dual graph convolutional network to capture both local and global information with a more effective multi-view fusion mechanism, thus providing better performance. LOSIT performs better than SSEGAT due to its knowledge integration through reinforcement learning and attention mechanisms. SSEGCN performs better than SSEGAT on the Restaurant and Laptop datasets due to its aspect-aware attention mechanisms and GCN to fuse sentence information. The addition of semantic knowledge can assist in modeling a better comprehension of what words mean and dig into the deeper features of the sentences. Dependencies can capture the relationships between modifiers and modified words, regardless of the distance between aspectual terms and opinion words. Thus, from a linguistic compositional perspective, the combination of knowledge can improve the catch of affective information.