## 4 Results and Discussion
### F1-Score Variants  
The following figures comprehensively compare the different F1-score variants--micro, macro, and weighted--used in this study. This analysis aims to illustrate how each metric performs under various conditions and to identify which F1-score variant most effectively captures the nuances of emotion classification across the datasets. The empirical results provide insights into the performance of each metric, highlighting their strengths and limitations in different dataset conditions, principally in terms of class balance and imbalance. Figure 14a illustrates the overlap between the macro, micro, and weighted F1-scores for the XED lexicon applied to the XED dataset. The maximum values of these metrics are close to each other, indicating a similar level of performance at certain thresholds. However, in the case of the lexicon and dataset with aggregated negative emotions, the macro F1-score consistently shows lower values compared to the micro and weighted F1-scores, which exhibit higher values across the evaluated thresholds (see Figure 14b). Table 4 presents the data from Figure 14 in more detail. Figure 14: Performance of XED distilled lexicon applied to XED dataset. (**a**) Chart that depicts micro, macro, and weighted F1-scores for the XED distilled lexicon, applied to the XED dataset across various thresholds; (**b**) chart that depicts micro, macro, and weighted f1-scores for the XED Aggregated distilled lexicon, applied to the XED Aggregated dataset across various thresholds. Figure 13: Precision–recall curves for three different emotions analyzed using the XED Aggregated distilled lexicon and applied to the GoEmotions Aggregated dataset. Each graphic represents the precision–recall curve for a specific emotion, with points on the curve representing different thresholds. Emotions include (**a**) joy, (**b**) surprise, and (**c**) negative. Figure 15a demonstrates variations in performance across different F1-scores when the XED lexicon is applied to the GoEmotions dataset. The macro F1-score is notably lower compared to the micro and weighted F1-scores. Specifically, the macro and the micro F1 do not reach the benchmark value of 0.53, which suggests suboptimal model performance. In contrast, the weighted F1-score indicates an acceptable performance level, at the 0.3 threshold. The XED aggregated lexicon applied to the GoEmotions aggregated dataset has a better performance in the minority class, as the macro F1-score shows higher levels (see Figure 15b). Table 5 present the data shown in Figure 15.  
Figure 15c for the GoEmotions distilled lexicon applied to the GoEmotions dataset presents a lower macro F1. In the case of the weighted F1, the lower thresholds show a higher value; then, for the higher value thresholds, the micro F1 has higher values (see Table 6). This performance pattern is mirrored in the results from the GoEmotions aggregated lexicon applied to the aggregated GoEmotions dataset, shown in Figure 16b and Table 6. Similar trends across the macro, micro, and weighted F1-scores are observed here. This underscores the impact of lexicon and dataset configuration and how aggregating the negative emotions in one class, as well as having a more balanced dataset, has an impact on the effectiveness of different F1 scoring methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**XED Distilled Lexicon Applied to**} & \multicolumn{2}{c}{**XED Aggregated Distilled Lexicon Applied to**} \\  & \multicolumn{2}{c}{**XED Dataset**} & \multicolumn{2}{c}{**XED Aggregated Dataset**} \\ \hline
**Threshold** & **Macro F1** & **Micro F1** & **Weighted F1** & **Macro F1** & **Micro F1** & **Weighted F1** \\ \hline
0.000 & 0.347 & 0.349 & 0.356 & 0.499 & 0.545 & 0.643 \\ \hline
0.100 & 0.365 & 0.367 & 0.373 & 0.515 & 0.568 & 0.653 \\ \hline
0.200 & 0.430  
Figure 17a evaluates the performance of the GoEmotions lexicon when applied to the XED dataset, revealing that all F1-scores--micro, macro, and weighted--show values below 0.53 (see Table 7).