{
  "id": 17929,
  "url": "https://arxiv.org/abs/2608.07251v1",
  "title": "Reading Copom's Tone: A Weighted LLM Framework for Hawkish-Dovish Sentiment, Forward Guidance, and Uncertainty",
  "summary": "This paper documents an applied natural-language-processing framework for measuring the tone of Brazilian Monetary Policy Committee (Copom) statements. The project is explicitly inspired by iSent, Itaú's Central Bank sentiment classifier, particularly its sentence-level division of official communication into hawkish, dovish, neutral, and out-of-context classes. The implementation extends that idea in three directions. First, an LLM identifies short hawkish and dovish expressions and assigns eac",
  "authors": "Gabriel de Macedo Santos",
  "category": "research",
  "topics": "regulation",
  "orgs": null,
  "regions": "latam",
  "published_at": "2026-08-07T14:08:31.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17929",
  "original_url": "https://arxiv.org/abs/2608.07251v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}