Public articles linked to the same research event.
PloS one The study scraped 15,300 HMPV-related comments from YouTube news channels in 2024-2025, retained 9,758 after preprocessing, generated weak sentiment labels with VADER, compared six transformer models (ELECTRA, RoBERTa, ALBERT, DistilBERT, XLNet, BERT), found XLNet best at 93.50% accuracy, and used SHAP's PartitionExplainer to produce word-level attributions on correctly classified samples, showing words such as "flu" and "fear" driving negative predictions, "save", "help" and "mad" associated with neutral, and "falling", "save", "god" and "america" associated with positive.
The study scraped 15,300 HMPV-related comments from YouTube news channels in 2024-2025, retained 9,758 after preprocessing, generated weak sentiment labels with VADER, compared six transformer models (ELECTRA, RoBERTa, ALBERT, DistilBERT, XLNet, BERT), found XLNet best at 93.50% accuracy, and used SHAP's PartitionExplainer to produce word-level attributions on correctly classified samples, showing words such as "flu" and "fear" driving negative predictions, "save", "help" and "mad" associated with neutral, and "falling", "save", "god" and "america" associated with positive.
The study scraped 15,300 HMPV-related comments from YouTube news channels in 2024-2025, retained 9,758 after preprocessing, generated weak sentiment labels with VADER, compared six transformer models (ELECTRA, RoBERTa, ALBERT, DistilBERT, XLNet, BERT), found XLNet best at 93.50% accuracy, and used SHAP's PartitionExplainer to produce word-level attributions on correctly classified samples, showing words such as "flu" and "fear" driving negative predictions, "save", "help" and "mad" associated with neutral, and "falling", "save", "god" and "america" associated with positive.
The study scraped 15,300 HMPV-related comments from YouTube news channels in 2024-2025, retained 9,758 after preprocessing, generated weak sentiment labels with VADER, compared six transformer models (ELECTRA, RoBERTa, ALBERT, DistilBERT, XLNet, BERT), found XLNet best at 93.50% accuracy, and used SHAP's PartitionExplainer to produce word-level attributions on correctly classified samples, showing words such as "flu" and "fear" driving negative predictions, "save", "help" and "mad" associated with neutral, and "falling", "save", "god" and "america" associated with positive.