## 5 Experimental Results
### Experiment 2: Ensemble Learning via Combining Different GNN Structures  
As seen in Table 4, in all cases of using ensemble learning techniques, the accuracy and performance of the model increases. According to Tables 3 and 4, we highlight the following: In the Digikala-2 Class dataset, we reached a 91% accuracy using Pars(Bert)+GCN in imbalanced data by using the ensemble learning technique, and this accuracy increased to 93% thanks to ensemble learning. In the Digikala-3 Class dataset, we reached about 64% of accuracy using ParsBert+GCN and the ensemble learning technique, and this accuracy reached about 68% thanks to ensemble learning. It is worth noting that, in the three-class dataset, 50,331 data points were allocated for training, while 12,583 data points were designated for testing. The number of extracted words was 7448, so the number of nodes was 70,362. For the two-class dataset, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model (Graph)**} & \multicolumn{4}{c}{**Digikala**} \\ \cline{2-5}  & \multicolumn{2}{c}{**2 Class**} & \multicolumn{2}{c}{**3 Class**} \\ \cline{2-5}  & **Balance** & **Imbalance** & **Balance** & **Imbalance** \\ \hline GCN (G1) & 66 & 89 & 54 & 64 \\ \hline GAT (G2) & 64 & 81 & 48 & 59 \\ \hline GIN (G3) & 63 & 78 & 51 & 55 \\ \hline GCN (G4) & 64 & 86 & 53 & 67 \\ \hline \multirow{3}{*}{Ensemble Learning} & \multirow{3}{*}{69} & Accuracy = 93.2 \(\pm\) 0.02 & \multirow{3}{*}{58} & Accuracy = 68.4 \(\pm\) 0.09 \\  & & \(F_{1}\) = 77 & & \(F_{1}\) = 58 \\ \cline{1-1}  & & Precision = 78 & & Precision = 52 \\ \cline{1-1}  & & Recall = 79 & & Recall = 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy (%) of ensemble learning techniques (mean \(\pm\) standard deviation for the last row). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Digikala**} \\ \cline{2-5}  & \multicolumn{2}{c}{**2 Class**} & \multicolumn{2}{c}{**3 Class**} \\ \cline{2-5}  & **Balance** & **Imbalance** & **Balance** & **Imbalance** \\ \hline \multirow{3}{*}{ParsBERT} & \multirow{3}{*}{68} & Accuracy = 87 & \multirow{3}{*}{57} & Accuracy = 62 \\  & & \(F_{1}\) = 72 & & \(F_{1}\) = 55 \\  & & Precision = 69 & & Precision = 52 \\  & & Recall = 80 & & Recall = 54 \\ \hline \multirow{3}{*}{ParsBERT+GCN} & \multirow{3}{*}{70} & Accuracy = 91.1 \(\pm\) 0.06 & \multirow{3}{*}{58} & Accuracy = 63.9 \(\pm\) 0.09 \\  & & \(F_{1}\) = 74 & & \(F_{1}\) = 55 \\  & & Precision = 71 & & Precision = 52 \\  & & Recall = 80 & & Recall = 59 \\ \hline \multirow{3}{*}{BERT} & \multirow{3}{*}{57} & Accuracy = 81 & \multirow{3}{*}{54} & Accuracy = 56 \\  & & \(F_{1}\) = 68 & & \(F_{1}\) = 54 \\ \cline{1-1}  & & Precision = 66 & & Precision = 51 \\ \cline{1-1}  & & Recall = 74 & & Recall = 55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy (%) of the proposed model on the Digikala dataset (mean \(\pm\) standard deviation for the ParsBERT+GCN and BERT+GCN experiments). 42,446 data points were assigned to training, while 10,612 data points were reserved for testing. The number of extracted words was 4129, so the number of nodes was 57,187. It is worth recalling that, in this experiment, the size of the training sets associated to the rows \(G_{1},\ldots,G_{4}\) in the table are smaller than the size of the training set of the ParsBERT+GCN experiment in Table 3, since in those cases training was conducted on the mentioned subgraphs. We now briefly address the statistics of our above-mentioned ensemble model. In order to obtain a better understanding of the model, we repeated the above experiments and ran our model several times, each time with a different model weights initialization, and obtained the accuracy and other parameters of every repeated experiment to see how robust the results were. As mentioned in Table 4, the mean of the results (accuracies) is 93.2 with a standard deviation of less than 0.02 on the 2 Class dataset.