| Resumen |
This paper presents the results of our participation in the shared task Multilingual Hope Speech detection aimed at classifying texts into hope and non-hope categories. The task involved two datasets, one in English and the other in Spanish. We used the SVM algorithm for the English data and the KNN algorithm for the Spanish data. Our approach achieved the third place on both datasets. Specifically, our SVM-based approach achieved an F1 score of 0.49, while our KNN-based approach achieved an F1 score of 0.74. Our results suggest that cross-lingual classification of hope and non-hope texts is a challenging task, particularly due to the linguistic differences between languages. Nevertheless, our results demonstrate the effectiveness of the SVM and KNN algorithms for this task, highlighting the importance of selecting appropriate algorithms for different languages. Overall, this paper contributes to the growing body of research on cross-lingual text classification and provides insights for future work in this area. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). |
