Sentiment Monitoring

When to Use

• Founder wants to track what customers and the public are saying about their product
• Founder wants to catch bad reviews early and respond before they spread
• Founder wants to understand community sentiment trends over time
• Founder wants to monitor specific review platforms for new reviews

This is different from review-mining (mining competitor reviews for pain points). This skill monitors your OWN product's reputation.

Context Required

• Product name and any common misspellings or abbreviations
• Platforms to monitor — the founder must provide the list of places to watch. Common options:
  • Product Hunt (product page reviews and comments)
  • Google Maps / Google Business reviews
  • G2, Capterra, TrustRadius
  • Trustpilot
  • App Store / Play Store
  • Reddit mentions
  • Twitter/X mentions
  • Hacker News mentions
  • Industry-specific forums
• Monitoring frequency (daily for post-launch, weekly for steady state)
• Response policy — does the founder want draft responses for negative reviews?
• Escalation threshold — what severity warrants immediate attention?

Workflow

1. Set up the monitoring list — the founder provides which platforms to watch. For each platform, note:
   • Direct URL to the product's review/listing page
   • Current rating and review count (baseline)
   • How to check for new reviews (RSS, manual, API, or alert tool)
2. Define the severity scale — categorize incoming sentiment:
   • Critical (respond within 24h): public accusations of data loss, security issues, billing fraud, or legal threats. 1-star reviews with detailed complaints that could go viral.
   • Negative (respond within 48h): legitimate complaints about bugs, missing features, poor support, or pricing frustration. 1-2 star reviews.
   • Mixed (respond within 1 week): 3-star reviews with constructive feedback. "Good product but..."
   • Positive (acknowledge): 4-5 star reviews. Thank the reviewer, ask for referrals.
3. Scan platforms — check each platform on the founder's list for new reviews, mentions, or discussions since the last scan.
4. Analyze each finding — for every new review or mention:
   • Platform and date
   • Sentiment: positive / mixed / negative / critical
   • Core issue: what specifically is the person saying (quote verbatim)
   • Validity: is this a legitimate product issue, user error, or bad-faith review?
   • Impact: how visible is this? (high-traffic platform, many upvotes, or buried)
   • Pattern: does this match other recent complaints? (signals a systemic issue)
5. Draft responses — for negative and critical reviews, draft a response that:
   • Acknowledges the issue without being defensive
   • Shows the complaint was heard and understood
   • Offers a specific next step (DM, email, fix timeline)
   • Is written in the founder's voice, not corporate PR speak
6. Flag patterns — if 3+ reviews mention the same issue, escalate it as a product issue, not just a review problem.
7. Generate the sentiment report — summary of findings with trends.

Output Format

## Sentiment Report — [Date Range]

### Overview
- **Reviews scanned:** [count across all platforms]
- **New since last scan:** [count]
- **Sentiment breakdown:** [X positive, Y mixed, Z negative, W critical]
- **Average rating trend:** [up/down/stable vs. last period]

### Critical & Negative Items (action required)

**[Platform] — [Star Rating] — [Date]**
> "[Verbatim quote or summary]"
- **Core issue:** [what they're actually complaining about]
- **Validity:** [Legitimate / User error / Bad faith]
- **Pattern:** [First mention / Recurring — also seen on X, Y]
- **Suggested response:**
  > [Draft response in founder's voice]

### Emerging Patterns
| Issue | Mentions This Period | Platforms | First Seen | Trend |
|-------|---------------------|-----------|------------|-------|
| [Issue] | [count] | [platforms] | [date] | [new / growing / stable] |

### Positive Highlights
- [Platform]: "[positive quote]" — consider using as testimonial
- [Platform]: "[positive quote]" — share on social

### Recommended Actions
- [ ] Respond to [N] critical/negative reviews (drafts above)
- [ ] Investigate [issue] — mentioned [N] times across [platforms]
- [ ] Request reviews from happy customers to offset [negative trend]

Frameworks & Best Practices

Response principles for negative reviews:

• Speed matters — respond within 24-48 hours. Unanswered negative reviews signal "they don't care."
• Acknowledge, don't argue — "I hear you" beats "Actually, you're wrong" every time
• Take it offline — "I'd love to look into this — can you email me at founder@company.com?" moves the conversation out of public view
• Be the founder — sign with your name and title. "— Alex, CEO" hits differently than a generic support reply
• Fix the issue, then update — come back to the review after fixing the problem: "We shipped a fix for this last week"

Platform-specific notes:

When negative reviews are actually gifts:

• Specific, actionable complaints point to real product gaps — treat them as free user research
• A pattern of "love the product but X is broken" means you have product-market fit with a fixable issue
• No negative reviews at all usually means no one is using the product

Common mistakes:

• Monitoring without responding (worse than not monitoring)
• Getting defensive or arguing publicly with reviewers
• Only monitoring one platform (customers complain wherever they are, not where you're watching)
• Treating all negative reviews equally (a billing fraud accusation ≠ a UI complaint)
• Not feeding review insights back into the product roadmap

Related Skills

• review-mining — for mining COMPETITOR reviews (this skill monitors YOUR reviews)
• feedback-synthesis — for synthesizing feedback patterns into product decisions
• churn-analysis — negative reviews often correlate with churn signals
• community-discovery — to find communities where people discuss your product

Examples

Prompt: "Set up monitoring for our reviews. We're on Product Hunt, G2, Trustpilot, and the App Store."

Good output includes: Monitoring checklist for all 4 platforms with current baselines, severity scale customized to the product, and a template for the weekly sentiment report.

Prompt: "We got 3 bad reviews on G2 this week. Help me respond."

Good output includes: Analysis of each review (core issue, validity, pattern detection), draft responses in the founder's voice, and a flag if the issues point to a systemic product problem.