## 5 Discussion  
The current section discusses the results of the analysis carried out from an experimental design point of view. With respect to **the number of hidden layers** for feature representation, we first studied the effect of the number of hidden layers in both the generator and the discriminator. We varied the number of hidden layers, ranging from one to four first at the generator only, then at the discriminator, and, finally, at both networks simultaneously, and observed how the changes in feature representation affect the classification performance. This evaluation is conducted exclusively on the validation set of all three examined dataset collections. In most cases, two or three layers provide the best performance. Despite the theoretical existence of unique solutions, GAN training is challenging and often unstable for several reasons, as reported in [5; 15; 75]. One approach to improve GAN training is to assess the empirical \"symptoms\" that might be experienced during training. These symptoms include: difficulties in getting the pair of models to converge [5], the generative model collapsing to generate very similar samples for different inputs [15], and, finally, the discriminator loss converging quickly to zero [75], providing a non-reliable path for gradient updates to the generator. Based on our extensive experiments, we did not notice any significant performance increase due to the increase in the number of hidden layers in our proposed network (neither in the generator nor in the discriminator model for all three different types of datasets). Our initial aim was to select for each type of dataset the ideal \"capacity\" of hidden layers for both models of our network. However, our results are aligned with the work of [76], according to which even if providing successful solutions to the above-presented challenging issues when training GANs, building the GAN network still depends highly on the examined task/application. To investigate **the performance results** achieved by our proposed model, we used all of the test data from the three datasets as queries, and we compared them to a fraction of the closest type of text of the examined dataset in the three training sets, from which we calculated the similarity based on the cosine similarity between the vector representations. The average number of returned documents and sentences having the same label as the query document (precision) were recorded. As far as the selected evaluation metric are concerned, we maintain that there is a good reason to consider precision-recall due to the issue of dataset imbalance. For information retrieval, precision is a measure of result relevancy, while recall is a measure of how many truly relevant results are returned. Thus, the precision-recall curves presented in Figure 3 show the trade-off between precision and recall for different thresholds. We observed that we achieved high scores for both precision and recall for the Movie Reviews Dataset (sentence level), suggesting that our proposed GAN architecture returns accurate results (high precision), as well as returning a majority of all positive results (high recall). Additionally, we observed that our aspect-level GAN system for the same recall value has 0.2% higher precision results compared to the document-level GAN system. Apart from that, our results suggest that considering that our proposed document-level GAN model achieves high precision but low recall, it returns very few results, but most of its generated documents are correct when compared to the training ones. Finally, after comparing our proposed model performance with the baseline performance, we observed that, in all cases, all three different benchmarks for the same recall values given in Table 3 outperform our GAN baseline. Overall, the most ideal system among the three, achieving both high precision and high recall while return many results generated correctly, is the sentence-level GAN model based on the Movie Reviews Dataset.