## 4 Experiment
### Case Study  
As a visual demonstration of the model's capabilities, we further validate our approach with a case study, we analyze a few randomly selected sentences from the Restaurant dataset, and Table 4 exhibits the result data, where the bold part of the sentence represents the aspect word, \(\times\) is a wrong prediction, and \(\surd\) denotes a correct prediction. Table 4 shows the results of randomly selected cases analyzed using different models. The first sentence has a neutral sentiment polarity. The sentiment of this type of sentence is not particularly obvious, and the model may change certain words in the sentence and thus misiudge the neutral sentiment. For example, in our first sentence, the word \"but\" is used as a progressive, complementing and emphasizing what has been mentioned before, but it may be mistaken for a negative attitude. In the third sentence, the AOA model does not catch the keyword \"are not\", thus leading to a prediction error. In this sentence, \"terrible\" is a pejorative word, but it is preceded by the negative word \"not\", so it is a positive sentiment overall. DualGCN integrates syntactic knowledge and semantic information for sentiment classification and utilizes orthogonal and differential regularizers to process semantically related items, which outperforms the predictions of R-GAT and AOA, but also suffers from prediction errors. In contrast, the syntactic structure and semantic associations of the words are the focus of our modeling attention and obtain the correct sentiment classification results after comprehensive consideration. These examples emphasize the importance of recognizing the overall utterance information in a sentence for the ALSC task, and our model can effectively capture comprehensive utterance information, utilizing the complementary nature of syntactic structure and semantic information, and accurately predicting sentiment polarity.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Sentence**} & **Actual** & **Ours** & **DualGCN** & **R-GAT** & **AOA** \\ \hline It was enjoyable, but none of the **scents** amazed me. & Neut. & Neut.\(\surd\) & Nega.\(\times\) & Nega.\(\times\) & Posi.\(\times\) \\ The **waiter** poured water on my hand and walked away. & Nega. & Nega.\(\surd\) & Neut.\(\times\) & Nega.\(\surd\) & Nega.\(\surd\) \\ The **prices** are not terrible. & Posi. & Posi.\(\surd\) & Posi.\(\surd\) & Posi.\(\surd\) & Nega.\(\times\) \\ Fantastic **ambiance**, but the **staff** was absolutely terrible! & Posi., \(\surd\) & Posi.\(\surd\) & Posi.\(\surd\) & Posi.\(\surd\) & Nega.\(\times\) \\ Despite the high **prices**, **dining** here is consistently & Nega. & Nega.\(\surd\) & Nega.\(\surd\) & Nega.\(\surd\) & Nega.\(\surd\) \\ satisfying. & Posi. & Posi.\(\surd\) & Posi.\(\surd\) & Nega.\(\times\) & Posi.\(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the case study experiment. “Neut.” is Neutral, “Nega.” is Negative, and “Posi.” is Positive. Bolded are the aspect words in the sentences. “\(\surd\)” indicates a correct result for sentiment categorization and “\(\times\)” indicates an incorrect result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Twitter**} \\ \cline{2-7}  & **Accu.** & **Macro-F1** & **Accu.** & **Macro-F1** & **Accu.** & **Macro-F1** \\ \hline w/o SA & 79.05 & 75.71 & 84.97 & 78.98 & 75.85 & 74.32 \\ w/o SE & 80.56 & 77.00 & 86.88 & 81.16 & 76.59 & 75.11 \\
**Ours** & **81.80** & **78.63** & **87.26** & **83.08** & **78.35** & **77.27** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment results.