Sentiment Tracker

Monitor brand sentiment, crypto opinions, and product perception across social media with automated tracking, alerts, and multi-entity dashboards.

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Install skill "Sentiment Tracker" with this command: npx skills add sentiment-tracker

Sentiment Analysis

Track what people say about anything — brands, crypto, products, competitors — across Twitter/X, Reddit, YouTube, Hacker News, and news sites.

One-shot analysis for quick checks. Scheduled monitoring for ongoing tracking. Multi-entity dashboards to compare multiple things at once.

Setup

On first use, read setup.md and follow its guidelines. Data is stored locally in ~/sentiment-analysis/.

When to Use

User wants to know public opinion about something. Could be:

  • "What are people saying about [brand]?"
  • "How's sentiment on [crypto] right now?"
  • "Monitor [product] mentions and alert me on negative spikes"
  • "Compare sentiment: [brand A] vs [brand B]"

Architecture

Data lives in ~/sentiment-analysis/. See memory-template.md for setup.

~/sentiment-analysis/
├── memory.md           # Config, entities, preferences
├── entities/           # One file per tracked entity
│   ├── brand-name.md
│   └── crypto-xyz.md
├── reports/            # Generated analysis reports
│   └── YYYY-MM-DD-entity.md
└── alerts.md           # Alert history

Quick Reference

TopicFile
Setup processsetup.md
Memory templatememory-template.md

Core Rules

1. Source Diversity Matters

Never rely on a single platform. Each source has bias:

  • Twitter/X: Real-time, emotional, viral content
  • Reddit: Longer discussions, honest opinions, niche communities
  • YouTube: Comments show product experiences
  • Hacker News: Tech-focused, skeptical, early adopter views
  • News sites: Official narratives, PR-filtered

Use at least 2-3 sources per analysis. Note source distribution in reports.

2. Time Windows Change Everything

Sentiment shifts fast. Always specify and report time window:

  • Last 24h: Breaking news, viral events
  • Last 7d: Weekly trends, sustained campaigns
  • Last 30d: Product launches, seasonal patterns

Default: Last 7 days unless user specifies otherwise.

3. Quantify, Don't Guess

Every report includes concrete metrics:

📊 Entity: [Name]
🕐 Period: [Date range]
📈 Volume: [X mentions found]
😊 Positive: XX% | 😠 Negative: XX% | 😐 Neutral: XX%

Top Themes:
1. [Theme] — XX mentions, XX% negative
2. [Theme] — XX mentions, XX% positive

Notable Posts:
- [Quote] — [Platform, engagement]

4. Alerts Are Specific

Don't alert on every change. Track baselines and alert on:

  • Negative spike >20% above baseline
  • Viral negative post (>10x normal engagement)
  • New negative theme appearing
  • Competitor positive spike

5. Multi-Entity Comparison

When tracking multiple entities, always show relative performance:

📊 Sentiment Comparison (Last 7d)

| Entity | Volume | Positive | Negative | Trend |
|--------|--------|----------|----------|-------|
| Brand A | 1,240 | 62% | 18% | ↗️ +5% |
| Brand B | 890 | 45% | 32% | ↘️ -8% |

6. Scheduled Monitoring

For ongoing tracking, use cron. Default schedules:

  • Critical entities: Daily at 09:00
  • Regular entities: Every 3 days
  • Background entities: Weekly

Store schedule in memory.md. Deliver reports to user's preferred channel.

7. Save Everything

After each analysis:

  1. Update entity file with new data
  2. Compare to previous analysis
  3. Note trend changes
  4. Archive raw findings

Common Traps

  • Single-source analysis → Completely skewed view. Reddit hates everything, Twitter loves drama. Always cross-reference.
  • No time window → "Sentiment is positive" means nothing without dates. A product can be loved one week, hated the next.
  • Vanity metrics → High volume ≠ positive sentiment. 1000 mentions with 80% negative is worse than 100 mentions with 60% positive.
  • Ignoring context → A spike in "crypto X is dead" might be sarcasm or memes. Read actual posts, not just keyword counts.
  • Alert fatigue → Alerting on every fluctuation makes users ignore alerts. Only signal meaningful changes.

External Endpoints

EndpointData SentPurpose
Search engines (via web_search)Query textFind mentions
Social platforms (via web_fetch)URL requestsRead content

No API keys required. No data stored externally. All analysis happens locally.

Security & Privacy

Data that leaves your machine:

  • Search queries sent to web search (query text only)
  • URL requests to public posts (reading only)

Data that stays local:

  • All entity tracking in ~/sentiment-analysis/
  • Historical sentiment data
  • Alert configurations

This skill does NOT:

  • Require accounts on any platform
  • Store data on external servers
  • Send personal information anywhere
  • Access private/protected content

Related Skills

Install with clawhub install <slug> if user confirms:

  • analytics — web traffic and conversion data
  • branding — brand strategy and guidelines
  • monitor — system and service monitoring

Feedback

  • If useful: clawhub star sentiment-tracker
  • Stay updated: clawhub sync

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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