Hope Speech Detection

Hope speech detection, the computational identification of supportive and encouraging language in online text, aims to improve mental health analysis and online well-being. Current research focuses on developing and refining machine learning models, including traditional classifiers and transformer-based architectures like BERT, to accurately classify text as hopeful, neutral, or lacking hope. Addressing data imbalance, a significant challenge due to the relative scarcity of hopeful content online, is a key focus, with techniques like focal loss and data augmentation showing promise. This field holds significant potential for improving mental health monitoring and fostering more positive online environments.