| Resumen |
Hope is a positive mood rooted in the expectation of favorable results in one’s life or the world in general, and it is both expressed in the present and the future. We make use of traditional machine learning models and transformer algorithms such as Support Vector Machine (SVM), Random Forest (RF), and a transformer-based BERT model for hope speech detection using an English dataset for binary hope speech detection collected from Twitter, which is provided by HOPE at the IberLEF 2024 share task organizers. Our experiment using the BERT model achieved a macro-average F1-score of 0.85 in the binary classification task, and when compared to the above-mentioned machine learning models, it consistently outperforms them. This study provides valuable insights into addressing hope speech and explores the effectiveness of advanced NLP techniques in promoting positive communication online. © 2024 Copyright for this paper by its authors. |
