Stacked Transformer Ensemble for Hope Speech Detection in High-Resource Languages

The rapid growth of user-generated content on social media platforms has escalated both the spread of harmful speech and the emergence of positive discourse, such as hope speech messages that offer encouragement, support, and optimism to individuals and communities facing challenges. While significant research has been conducted on detecting hate speech and toxicity, the detection of hope speech remains underexplored. In this paper, we combine several advanced deep learning architectures for hope speech detection in high-resource languages such as English. Our approach can effectively classify social media comments as either hopeful or non-hopeful speech. Experimental evaluations on the publicly available HopeEDI dataset demonstrate that our deep learning model consistently outperforms individual models, achieving weighted average F1-scores of 0.94 for the English subdataset. The results validate the effectiveness of combining complementary models to enhance performance, especially in high-resource settings.