# An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets
## 6 Experimental Results and Discussion
### Classification Results
Table 9 shows the results of each partition for both databases regarding AUC and F1 score metrics. We highlight the INTER+P4C results in Table 9a,b. In this way, we can compare each database's differences easily when using the same combination. It is essential to compare the average (AVG) results and their standard deviation (STD); this allows us to understand the differences between each database. From Table 9, we can obtain the following observations:  
* For EXD, the combinations with the lowest STD in both metrics are W2V+P4C and BOW+P4C, while for PXD, they are TFIDF+P4C and TDIDF+RUS. * For EXD, the combinations with the highest AVG in both metrics are INTER+P4C and BOW+C45, while for PXD, they are TFIDF+C45, BOW+P4C. * For EXD, the best combination in terms of AVG and STD is BOW+C45, while for PXD, it is TFIDF+P4C. The difference between these models is, on the one hand, that BOW+C45 in EDX achieves 0.040 more than TFIDF+P4C in both AUC and F1 score metrics. On the other hand, TFIDF+P4C in PDX has approximately 0.003 fewer STD than BOW+C45 in EXD. Table 10 shows a comparison of results between EXD and PDX for the combinations of the embedding method and classifier that are interpretable. When we compare both AVG and STD for the interpretable combinations present in Table 10 we can observe the following characteristics. * The best interpretable combinations are INTER+P4C and INTER+C45 for both databases. INTER+P4C shows a difference of 0.050 or greater in both AUC and F1 scores than INTER+C45 in EXD. This difference is greater in PXD. * On the one hand, on AVG, the results obtained in AUC are higher in EXD than in PXD. On the other hand, the AVG results for the F1 score are higher in EXD only for the P4C and C45 classifiers. * The STD in EXD for the INTER+P4C and INTER+C45 combinations is less than in PXD for both AUC and F1 score metrics. * The combination with the lowest STD is INTER+UND using PXD, while the combination with the highest STD is INTER+P4C using PXD. With the above observations, we can conclude that the results obtained by INTER+P4C are better than the other interpretable combinations. Furthermore, INTER+P4C has a higher AVG, and it is more robust according to their STD in EXD than in PXD. Notice that Table 11 shows very relevant information for the analysis of results. In this table, we can observe exceptional characteristics. Let us first analyze the results obtained by EXD (see Table 11a). * In two of the five partitions, the INTER+P4C configuration had the best results for both AUC and F1 scores (partition one and partition two). * In terms of F1 score, INTER+P4C always placed in the top five positions, while for AUC, there were two occasions in which it was in seven places. * When we compare the best partition of INTER+P4C, partition number two, against the best partition of BOW+C45, partition one. We can see that the best results of INTER+P4C are 0.11 AUC points and 0.15 F1 score points superior. * For all the embedding methods, the P4C classifier is always within the top ten of 20 regardless of partition or metric. Not only that, within the top five of each partition of each metric, there are always at least two combinations of embedding methods jointly with a classifier that uses P4C as a classifier.