# Text Classification Model Based on Graph Attention Networks and Adversarial Training
## 3 Results and Discussion
### Ablation Experiment  
To investigate the impact of the three distinct feature extraction capabilities of graph attention networks (GATs) incorporated into the model, ablation experiments were conducted using the THUCNews dataset and an automotive review corpus, as shown in Table 5. The ablation studies were performed by systematically removing different modules while keeping other components and parameters unchanged, to analyze their individual contributions to performance. In Table 5, TGAF represents the method proposed in this paper. TGAF-GAT1 indicates the removal of the Word-Character Containing Graph Network (GAT1); TGAF-GAT2 indicates the removal of the Word-Character Transition Graph Network (GAT2); TGAF-GAT3 indicates the removal of the Word-Character Lattice Graph Network (GAT3); and TGA represents the absence of adversarial training. The results show that removing any of the graph attention networks reduces the model's performance, underscoring the important role that each network plays in feature extraction. Among these, the performance drop is most significant with TGAF-GAT2, which highlights the critical role of the Word-Character Transition Graph Network in capturing the contextual information of vocabulary. This suggests that GAT2 is particularly effective in uncovering hidden correlations between vocabularies, contributing significantly to the overall ability of the model to interpret and classify text based on nuanced semantic relationships. This insight underscores the value of integrating diverse graph-based approaches to enhance the depth and accuracy of text classification models. Figure 6 shows the number of correctly classified news articles for each category by each model on the THUCNews dataset, displaying the test results of 1000 news texts for each category. The results show that the TGAF model performed better in classification, with over 950 correctly classified items in the education category. For categories similar to sports, the adversarial training added to the model exhibits strong robustness. The classification of other categories also demonstrated the effectiveness of the three graph attention networks added, and there were fewer instances of misclassifying different topic categories. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **THUCNews** & **Toutiao** & **Weibo** & **SougouCS** \\ \hline TGAF-GAT1 & 0.9250 & 0.8779 & 0.9844 & 0.8324 \\ TGAF-GAT2 & 0.9223 & 0.8752 & 0.9826 & 0.8291 \\ TGAF-GAT3 & 0.9253 & 0.8786 & 0.9853 & 0.8333 \\ TGA & 0.9288 & 0.8791 & 0.9862 & 0.8344 \\ TGAF & 0.9301 & 0.8812 & 0.9874 & 0.8351 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment. The size of the convolutional kernel is one of the key factors affecting model performance in text classification tasks. To further optimize model performance, this chapter conducts experiments to fine-tune the selection of convolutional kernel sizes. The results of the experiments are shown in Table 6. The model constructed in this chapter utilizes three CNN layers, necessitating a thorough investigation into the impact of various combinations of CNN kernel sizes on model performance. The first column in Table 5 lists different CNN size combinations; for example, (two, three, and four) indicates that the first convolutional layer of the model uses a kernel size of two, the second layer uses a size of three, and the third layer uses a size of four. The experimental results demonstrate that different combinations of convolutional kernel sizes significantly affect model performance. For the text dataset, the optimal classification performance was achieved with a kernel size combination of (three, five, and seven). The size of the convolutional kernels plays a crucial role in the model's performance; smaller kernels may reduce the model's ability to extract features, thereby negatively impacting classification performance.