Sentiment Methodology V2
Effective 2026-02-16T05:14:00Z, the sentiment scoring pipeline has been upgraded to Methodology V2. This is Phase 1 of a two-phase deployment.
What Changed
Both AI models in the sentiment scoring system have been replaced with newly trained versions. The scoring architecture remains the same - a primary model provides the default classification, while a secondary model provides confidence-based quality gating. Only the underlying models have changed.
Key Improvements
- Negative sentiment detection improved by approximately 50%. The previous models missed a significant portion of bearish tweets, systematically underreporting negative sentiment. The new models correct this bias.
- Model confidence quality nearly doubled. The secondary model’s confidence scores are now substantially more reliable, improving the accuracy of neutral and override decisions.
- Training data expanded by approximately 3.5x. The new models were trained on a much larger and more balanced dataset, with particular focus on improving coverage of negative examples.
Impact on Data
All data produced after the cutover timestamp reflects V2 methodology:
sentiment_model_versionchanged fromv1.0tov2.0methodology_regimefield added with valuev2- Negative sentiment ratios will be noticeably higher (correcting previous underreporting)
- Overall sentiment scores will shift closer to neutral (less positive bias)
- Post volume is unchanged in Phase 1
Cutover Details
| Detail | Value |
|---|---|
| Cutover timestamp | 2026-02-16T05:14:00Z |
| Version identifier | v2.0 |
| Regime field | methodology_regime: "v2" |
Historical data (since December 2025) remains available but should not be directly compared with V2 data without accounting for the methodology change. The methodology_regime field enables programmatic filtering.
Phase 2 (Pending)
A crypto relevance filter will be deployed in Phase 2, removing non-crypto noise before sentiment scoring. This will improve sentiment signal quality by ensuring only relevant posts are included in aggregated metrics.
Cutover timestamp: 2026-02-16T05:14:00Z.