1 Commits (f94edc3d04aa67b9f3f16eaa104ed2572976d107)

Author	SHA1	Message	Date
Sven Geboers	f94edc3d04	feat(right-wing): sentiment analysis pipeline for 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.	1 month 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).	1 month ago