Input cluster JSON:
{cluster_json}

You MUST extract a news signal from the headline AND summary. Do not leave entities empty when the text mentions obvious names.
Task:
1) infer the best top-level topic
2) extract concise entities from the cluster
3) assign sentiment from the wording/context
4) provide short keywords that justify the classification

Entity rules (strict):
- Use short strings (1-5 words).
- Include all obvious named entities mentioned in headline or summary: 
  named people, , named locations, organizations, ministries, presidents, leaders, wars/conflicts if named.
- Also include finance/crypto entities when present: BTC, ETH, Bitcoin, Ethereum, ETF, SEC, ECB, Fed, euro, inflation, rates.
- Prefer canonical entity forms over aliases when obvious (for example, use full organization or place names where helpful).
- Do NOT return empty entities if any such names/places appear.

Keyword rules (strict):
- Each keyword MUST be 1-2 words. Never 3+.
- Keywords are thematic search tags, NOT headline restatements or verb phrases.
- Good keywords: noun phrases or named concepts (e.g. "drone strikes", "energy infrastructure", "nuclear plant", "oil refinery").
- Bad keywords: full headline fragments, verb-heavy phrases, or anything over 2 words.
- Keywords should capture the *themes* of the story, not repeat entity names already in the entities list.
- Return 2-4 keywords. Fewer is better than bad ones.

Sentiment rules:
- positive: clearly encouraging, improving, or supportive tone
- negative: clearly alarming, worsening, severe, conflict, loss, risk, warning tone
- neutral: factual, balanced, or mixed
- sentimentScore must be a number from -1.0 to 1.0 and should reflect the sentiment label.

Return STRICT JSON with EXACT keys only:
{ topic, entities, sentiment, sentimentScore, keywords }
where topic is one of [crypto, macro, regulation, ai, other].