METHODOLOGY
How EihDah measures public reaction
We analyze a time-bounded sample of public comments, then report distribution, themes, and confidence alongside the source and collection window.
1. Collection
Supported public URLs are sampled within a disclosed window.
2. Classification
Language-aware models estimate positive, neutral, negative, and emotion signals.
3. Aggregation
Individual predictions become distributions, drivers, themes, and movement.
4. Quality checks
Low volume, language gaps, and model uncertainty reduce the quality indicator.
Data sources and limitations
The public MVP supports YouTube comment analyses. Results reflect the available sample and may not represent every viewer or the broader public. Automated sentiment can misread sarcasm, dialect, context, or coordinated behavior. EihDah does not infer factual truth, endorse a viewpoint, or score editorial quality.