Extremity Scorer (U4 enhanced):
- Now scores BOTH original motion text AND layman explanation separately
- Schema: text_score, text_explanation, layman_score, layman_explanation
- Text scores: 1→7, 2→33, 3→5, 4→5 (mild-to-moderate)
- Layman scores: 1→12, 2→20, 3→17, 4→1 (slightly milder)
Sentiment Analysis (U5 enhanced):
- Now scores BOTH original motion text AND layman explanation separately
- Schema: text_score, text_explanation, layman_score, layman_explanation
- Text sentiment avg: 0.294 (slightly positive)
- Layman sentiment avg: 0.416 (more positive - summaries tone down hostility)
Category Derivation (new):
- Two-phase LLM approach: derive taxonomy from sample, then apply to all
- Discovered 7 categories from 30-motion sample:
veiligheid/justitie, corona/pandemie, economie/belasting, klimaat/milieu,
defensie/buitenland, asiel/vreemdelingen, overig
- Applied to 50 motions with distribution shown in DB
- Adds category + category_explanation columns to right_wing_motions
Implements U5: sentiment_analysis.py uses LLM batch calls (fallback when no
local Dutch sentiment model is available) to score motion sentiment on [-1, 1]
scale.
Design:
- Prompt asks for sentiment from -1 (hostile/aggressive) to 1 (constructive)
- JSON schema enforces numeric score + Dutch explanation
- Batch size 10, max_workers 5 for parallel API calls
- Stores results in table
- Updates with avg_sentiment, sentiment_std,
pct_strongly_negative per year
Sample validation (50 motions): good variance across [-0.9, 1.0] range.
Sven Geboers