2 Commits (2a081ade25e8d86ee02500a393b28392e53a59f3)

Author	SHA1	Message	Date
Sven Geboers	fbf92c82cf	feat(right-wing): dual-scoring extremity/sentiment + derived categories ... 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	2 months ago
Sven Geboers	d2310edfc4	feat(right-wing): LLM-based policy extremity scoring ... Implements U4: extremity_scorer.py uses ai_provider.chat_completion_json_parallel with a JSON schema enforcing integer 1-5 + Dutch explanation. Design: - Batch size 10, max_workers 5 for parallel API calls - Prompt asks for concrete policy + radicalism score in Dutch - Stores results in table (motion_id, score, explanation, error) - Updates with yearly averages - Default sample=50 for validation; --sample -1 scores all motions Sample validation (50 motions): scores distributed 1→2, 2→34, 3→7, 4→7, yearly averages ~2.0-2.5 (mild-to-moderate radicalism).	2 months ago