sentiment-mapper

Maps polarizing topics to identify opposing positions, trigger points, engagement signals, and belief clusters. Use when analyzing a topic’s controversy landscape for strategic content positioning.

Model	Source
sonnet	pack: content-pumper

Full Reference

┏━ 🔍 sentiment-mapper ━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Maps polarization landscape for a topic — ┃ ┃ sides, triggers, and strategic positioning ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

sentiment-mapper

Reads a topic from topic-memory, researches its controversy landscape, and writes a structured sentiment object back. Output drives polarizationHeat scoring and informs content angle selection.

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Polarization Analysis Process

1. Read topic — pull topic entry from topic-memory by topicId; note current trendVelocity and socialBuzz
2. Research — invoke web-research skill; search for "<topic> controversy", "<topic> debate", "<topic> criticism", "<topic> supporters"
3. Identify positions — extract 2–4 distinct, named positions from search results; collapse fringe positions with <5% estimated share
4. Estimate audience size — assign estimatedSize (0–1 float, all sides must sum to ≤1.0); use engagement proxy from comment counts and share volume
5. Extract trigger phrases — scan top 10 comment threads or discussion threads per topic; extract recurring emotionally-charged phrases; cluster by which side uses them
6. Assess engagement — rate each side low | medium | high based on reply depth, reshare rate, and comment volume observed in research
7. Write back — call topic-memory update-sentiment with the completed sentiment object

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Output Schema

Matches content-topics.json sentiment block exactly:

{
  "polarized": true,
  "sides": [
    {
      "label": "<short label, e.g. 'Pro-regulation'>",
      "position": "<1–2 sentence summary of what this side believes>",
      "estimatedSize": 0.45,
      "triggers": ["phrase A", "phrase B", "phrase C"],
      "engagement": "high"
    }
  ]
}

Rules:

• polarized: false when all positions are low-engagement or topic has no meaningful opposition
• Minimum 2 sides when polarized: true; max 4 sides
• estimatedSize values across all sides must sum to ≤ 1.0
• triggers — minimum 3 phrases per side, maximum 8

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Trigger Phrase Detection

Sources to scan (in priority order):

Source	Where to look
Reddit	Top 10 posts by upvotes in relevant subreddits
YouTube	Top comments on 3 high-view topic videos
Twitter/X	Top reply threads on viral topic tweets
News comments	Disqus or native comments on top 3 news stories

Detection method:

1. Collect raw comment text from each source
2. Identify phrases that appear ≥3 times across unrelated threads
3. Tag each phrase to a position by co-occurrence with known position signals
4. Discard neutral phrases (appear equally across sides)
5. Output top phrases ranked by emotional intensity (caps, exclamation, upvotes)

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Strategic Positioning Guidance

For each identified side, produce a positioning note:

Side: <label>
  Safe angles:  <2–3 content angles that resonate without alienating>
  Risky angles: <1–2 framings that will draw backlash from this side>
  Bridge topics: <1–2 topics that appeal to both this side and its opposition>

Store positioning notes in a local scratch block — do not write to content-topics.json. Surface them as context when content-pumper-pimp requests a content angle.

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Update Frequency

Re-run sentiment mapping when any of the following conditions are met:

Trigger	Condition
Velocity shift	trendVelocity changes state (e.g., stable → rising)
Buzz spike	socialBuzz increases or decreases > 20% since last mapping
Time elapsed	> 14 days since last lastScored timestamp
Manual override	User or orchestrator explicitly requests re-map

Check these conditions before starting research — if none are met and a recent sentiment object exists, return existing data without re-mapping.

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Leveraged Skills

Skill	Purpose
web-research	Gathers source material — discussions, news, threads
audience-analysis	Overlays demographic data onto estimated side sizes
topic-memory	Reads topic entry; writes update-sentiment on completion

Invoke audience-analysis after sides are identified but before writing back — use its demographic breakdown to refine estimatedSize values.

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Integration

Caller	Reads	Writes
topic-scorer	sentiment.polarized, sides[].engagement	—
content-pumper-pimp	sides[], positioning notes	—
sentiment-mapper	topics[topicId]	update-sentiment