# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 5 Experiments and Results
### Results of FakeDetectionGCN Model  
Figure 7 shows the training loss for the 2-layer Bi-LSTM model applied to the two datasets. As mentioned earlier, the FakeDetectionGCN method computes the initial node features from the 300-dimensional GloVe vectors of each token. These features are subsequently refined into 300-dimensional embeddings, which serve as input for a GCN. The objective of the model is to minimize the loss between the input and output embeddings, ensuring the embeddings are representative of the original inputs.  
From the curves, we observe a steady decrease in training loss, indicating consistent learning across both datasets. The Salminen dataset shows a smoother and more rapid convergence compared to the OTT dataset. This difference could be due to the variation in dataset complexity and size.  
Table 7 summarizes the results of the FakeDetectionGCN model for different sample sizes of the two datasets. Figures 8-10 display the confusion matrices and PR curve plots of the datasets for visualizing the results.  
The OTT dataset at 80% sample size achieved an accuracy of 88.12%. It had a precision of 90.54% and a recall of 84.81%. This resulted in an F1 score of 87.58% and an AUC of 0.94. These results show that the model is quite precise, but it could improve in identifying true positives (real reviews).  
For the OTT dataset at 100% sample size, the model achieved an accuracy of 90.00%. Precision and recall were both 89.33%, leading to an F1 score of 89.33% and an AUC of 0.95. These figures indicate a balanced performance. They suggest that the model is effective in both identifying true positives and avoiding false positives as the data size increases.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline OTT (80\%) & 0.8812 & 0.9054 & 0.8481 & 0.8758 & 0.94 \\ OTT (100\%) & 0.900 & 0.8933 & 0.8933 & 0.8933 & 0.95 \\ Salminen (12.5\%) & 0.8960 & 0.8885 & 0.9094 & 0.8988 & 0.95 \\ Salminen (25\%) & 0.9070 & 0.9171 & 0.9086 & 0.9128 & 0.96 \\ Salminen (100\%) & 0.9233 & 0.9333 & 0.9152 & 0.924 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation results for OTT and Salminen datasets.  
Figure 7: Loss curves for computation of node feature matrix.  
For the Salminen dataset at 12.5% sample size, the accuracy was at 89.60%. The recall was 90.94%, which led to an F1 score to 89.88%. Although precision dropped slightly to 88.85% as compared to the OTT dataset, the overall performance improved, as shown by the AUC of 0.95.  
With the Salminen dataset at 25% sample size, there were improvements across all metrics. Accuracy was 90.70%, and precision reached 91.71%. The recall was at 90.86%, leading to an F1 score of 91.28% and an AUC of 0.96.  
In the Salminen dataset at 100% sample size, there was a further enhancement in performance. The accuracy reached 92.33%, and precision was very high at 93.33%. With a recall of 91.52%, the model had an F1 score of 92.40% and an AUC of 0.97.  
Overall, a trend is observed across the datasets. Increasing the proportion of the data in training enhances the model's ability to generalize. It also helps the model perform more consistently across all metrics.  
Figure 8: Results on the OTT dataset (100%).  
Figure 10: Results on Salminen dataset (25%).  
Figure 9: Results on Salminen dataset (12.5%).