## 4 Discussion  
Table 4 provides a summary of the results of previous studies of SA models with the positive, negative, and neutral classes, compared with the model that resulted in this study after the GA optimization. The first two models refer to classic ML approaches, while the last three refer to DL approaches. Finally, the last model refers to the architecture found in this study, revealing an apparent improvement from previous studies. The actual performance might be hinged by the particularities of the dataset used in each study, namely the data imbalance and the domain of the reviews. The results from [PERSON] et al. [29] were included despite being from an English dataset because the architecture developed in this study was greatly based on theirs. [PERSON] and [PERSON] [17] used the TweetSentBR dataset, [PERSON] and [PERSON] [19] scrapped the reviews available in the Google Play app store, and [PERSON] et al. [26] used the datasets compiled by [PERSON] and [PERSON] [16]. [PERSON] et al. [27] used the same dataset used in this study; therefore, it was the most relevant for comparison. Regarding the number of trainable parameters, the BERTimbau model trained with AdaBoost [27] provided the greatest performance with the same dataset, with a total of 220 million parameters. The model from this study is approximately five times smaller (41.1 million parameters), with an absolute loss in performance of 2.5% in accuracy and 2.3% in F1-score when compared to theirs. Therefore, the optimization process was valuable to increase the performance of the model while maintaining a relatively small number of trainable parameters, and provides better performance than in previous studies, including when compared to the BERTimbau model trained solely with transfer learning. Furthermore, the optimization could not surpass the performance of more complex training techniques such as boosting [27]. The GA was used to fine-tune the model's hyperparameters and identify optimal configurations, demonstrating effective optimization. This process can be time-consuming, and the improvements might sometimes be very subtle. The GA was stopped at the eighth generation when the PM had increased sufficiently and was also limited by time availability. Further generations might have potentially increased model performance, as depicted by the increase in diversity. Regarding the first research question, the results showed a viable level of performance, helped not only by the optimization process but also by the training configuration, namely the usage of the CSL scheme. The usage of LLMs may provide superior performance [27] but at the cost of a large number of parameters (five times bigger for a gain of 2.5% in accuracy). The possibility to develop comparable models with less computational costs provides more feasibility for lower-resource environments. This shows that the optimization of simpler models can lead to considerable gains in performance for the same problem, i.e., the Zomato Portugal dataset. \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Reference** & **Model** & **Accuracy** & **F1-Score** \\ \hline [PERSON], 2017 [17] & NB & 64.6 & 59.9 \\ [PERSON] et al., 2018 [18] & LR & 81.3 & 79.6 \\ \hline [PERSON], 2019 [25] & Self-AT-LSTM & 66.5 & 67.6 \\ [PERSON] et al., 2022 [26] & BERTimbau & 78.8 & 66.8 \\ [PERSON] et al., 2024 [27] & BERTimbau with AdaBoost & 84.0 & 77.7 \\ \hline This study & LSTM-SA & 81.5 & 75.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of models developed in previous studies. The accuracy and F1-score are considered the performance metrics, as these are the most common in the literature. For the second research question, the GA proved to provide significant improvements in the baseline and the best chromosome, which will depend on the available time for optimization and the quality of the search space. The greater limitation of discrete GAs is the manual definition of the optimization space, which may lead to bias or hinge optimal configurations.