## 4 Experimental Validation
### Experimental Results  
In order to conduct a comprehensive evaluation of our proposed method, we used three types of datasets from different domains. It is also important to note that the evaluated datasets, as described in Section 4.1, vary greatly in length of the analyzed preprocessed text, ranging from 18.821 and 10.000 to 3.825 documents and sentences, respectively. This diversity enabled us to evaluate the robustness of our proposed approach in multiple experimental settings. As of yet, there is no consensus regarding the best evaluation metric with respect to the GAN's performance ([PERSON] et al., 2017). Different metrics assess, both qualitatively and quantitatively, various aspects of the generation process, and it is unlikely that a single metric can cover all aspects. Currently, two widely accepted scores, Inception Score ([PERSON] et al., 2015) and Frechet Inception Distance, rely on pretrained deep networks to represent and statistically compare original and generated samples. However, these scores have been applied and tested only on the CV domain and not on SA tasks. As far as the SA tasks are concerned, accuracy is the most commonly used measure, taking into account the latest published works presented in Table 1, mainly when CNNs and/or RNNs are examined. Thus, we maintain that there is good reason to consider the other measures as well. Taking into account that our proposed GAN architecture is based on a variation of the energy-based GAN ([PERSON] et al., 2016) and to further make a direct comparison with the latter work, we decided to use the precision/recall curves measure to evaluate the performance of the proposed model used in our study. Precision is defined as the percentage of relevant items out of the \"top X\" items retrieved by our algorithm, while recall is the percentage of retrieved relevant items of all the relevant items in the dataset ([PERSON] et al., 2017). For completeness, we further decided to use the F-measure metric to evaluate our proposed system's performance. The F-measure enables factoring both precision and recall into a single value, thus representing their harmonic mean ([PERSON] et al., 2017). This measure is calculated using the following formula:  
\[F-measure=\frac{(1+\beta^{2})\times recall\times precision}{(\beta^{2}\times precision )+recall} \tag{12}\]  
In our experiments, we used \(\beta=1\), thus giving equal weight to the two measures. Although accuracy is the most commonly used measure for SA tasks, an additional reason to consider the above-presented measures is the issue of dataset imbalance ([PERSON] et al., 2017; [PERSON] et al., 2015; [PERSON] et al., 2015). To be more precise, in many cases, SA datasets suffer from imbalances in class distribution, where the number of instances belonging to one class significantly outnumbers that which belongs to another. With respect to the datasets we used to evaluate our system, the 20 Newsgroup dataset and the Finegrained Sentiment Analysis datasets contain such significant imbalances in class distributions. As such, the advantage of our selected evaluation measure is that it enables the evaluation of our performance on the top X instances that are relevant, sorted by their probability of belonging to a certain class of relevant instances. This measure is often used in information retrieval when measuring the performance of a query: as the user is not likely to review thousands of documents/sentences with respect to an aspect, the top ranking documents/sentences are more important and thus receive greater weight. This measure enables us to analyze the performance of the evaluated algorithm from multiple perspectives. Finally, it is also particularly useful for real-world applications, where often only the top X items are used in the evaluation process.