## 4 Results and Discussion
### Precision-Recall Curves  
Precision-recall (PR) curves were generated for each of the six emotions from Ekman and for the three aggregated emotion categories. The points on each curve represent different thresholds used in the evaluation. Figure 6 displays the precision-recall curves of all six emotions of the GoEmotions distilled lexicon, applied to the GoEmotions dataset. In the case of the emotion \"joy\", depicted in Figure 6d, the curve demonstrates high recall and precision, indicating better model performance for this emotion. Similarly, Figure 7 illustrates the precision-recall curve for GoEmotions Aggregated distilled lexicon and applied to the GoEmotions Aggregated dataset. It shows that when negative emotions are combined, the model's performance improves, evidenced by the curve's proximity to the top-left corner, signaling high precision and recall levels. Figure 8 depicts the precision-recall curves for the GoEmotions distilled lexicon applied to the XED dataset. This visualization reveals notably low recall and precision across the curves, indicating that the model is not effective for this particular application. Conversely, Figure 9 illustrates the precision-recall curve for the GoEmotions Aggregated distilled lexicon applied to the XED Aggregated dataset. In this scenario, the curve demonstrates an improvement in model performance, as evidenced by higher recall values and precision for the aggregated negative emotions (see Figure 9c). Figure 10 displays the precision-recall curves for the XED distilled lexicon applied to the XED dataset. The curves demonstrate high recall and precision for 'joy' (see Figure 10d) and the other emotions, indicating stronger model performance across different emotional categories. The precision-recall curves for the XED Aggregated distilled lexicon and applied to the XED Aggregated dataset (Figure 11) also show an improvement for the negative emotions, as shown in Figure 11c, compared to the non-aggregated curves. Figure 6: Precision–recall curves for six different emotions analyzed using the GoEmotions distilled lexicon and applied to the GoEmotions dataset. Each graphic represents the precision–recall curve for a specific emotion, with points on the curve representing different thresholds. Emotions include (**a**) anger, (**b**) disgust, (**c**) fear, (**d**) joy, (**e**) sadness, and (**f**) surprise. Figure 8: Precision–recall curves for six different emotions analyzed using the GoEmotions distilled lexicon and applied to the XED dataset. Each graphic represents the precision–recall curve for a specific emotion, with points on the curve representing different thresholds. Emotions include (**a**) anger, (**b**) disgust, (**c**) fear, (**d**) joy, (**e**) sadness, and (**f**) surprise. Figure 7: Precision–recall curves for three different emotions analyzed using the GoEmotions. 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, (**c**) negative. Figure 12 illustrates the precision-recall curves for the XED distilled lexicon applied to the GoEmotions dataset. These curves indicate higher precision and recall for the emotion 'joy' (see Figure 12d), suggesting effective model performance for this specific emotion. Similarly, the performance metrics for aggregated negative emotions shown in Figure 13c also show improvement. Figure 10: Precision–recall curves for six different emotions analyzed using the XED distilled lexicon and applied to the XED dataset. Each graphic represents the precision–recall curve for a specific emotion, with points on the curve representing different thresholds. Emotions include (**a**) anger, (**b**) disgust, (**c**) fear, (**d**) joy, (**e**) sadness, and (**f**) surprise. Figure 9: Precision–recall curves for three different emotions analyzed using the GoEmotions. Aggregated distilled lexicon and applied to the XED Aggregated dataset.