| Resumen |
Systems to regulate and remove hateful, abusive, and offensive content from the internet have been developed in the last several years. But occasionally, those in positions of authority abuse this type of censorship to thwart the democratic right to free speech. Consequently, studies must address online content that is uplifting, encouraging, and supporting from a positive reinforcement perspective. In this regard, HOPE_ IberLEF 2024 created a dataset to recognize positivity in social media comments to encourage those who need mindset treatments. It consisted of two tasks with main two aims. Task-1 is about hope speech for equality, diversity,and inclusion, and task-2 is about hope for an expectation for future desire. Then we have been involved in two tasks and propose the tasks with three algorithms, including Logistic regression, Word2Vec, and Transformer-base. Among these three algorithms, the model with Transformer-based outperformed all others. For task-1, our model achieved a 0.55 macro F1-score. For Polyhope binary data, the model achieved 0.75 and 0.82 macro F1- scores for Spanish and English respectively. Similarly, for polyhope multiclass data, our model achieved 0.48 macro F1-score and 0.55 F1-score in Spanish and English datasets respectively. © 2024 Copyright for this paper by its authors. |
