| name | sentiment-analysis |
| description | Analyzes customer sentiment from reviews, social media, and support tickets with trend tracking, theme categorization, and alert recommendations. Use for brand health monitoring. |
| allowed-tools | Read Write Glob |
| metadata | {"author":"matthewhitcham","version":"1.0"} |
Sentiment Analysis
When to Use This Skill
Use this skill when you need to:
- Analyze customer sentiment from reviews, social posts, or support tickets
- Track brand perception trends over time
- Categorize feedback themes to prioritize product or service improvements
- Set up a sentiment monitoring framework for ongoing use
DO NOT use this skill for social media content creation, customer service response writing, or NLP model building. This is for interpreting and acting on customer sentiment data.
Core Principle
SENTIMENT IS A LEADING INDICATOR — DECLINING SENTIMENT PREDICTS CHURN, NEGATIVE REVIEWS, AND REVENUE LOSS BEFORE THEY SHOW UP IN YOUR FINANCIALS.
Phase 1: Brief
Required Inputs
| Input | What to Ask | Default |
|---|
| Data sources | "Where is the feedback? (Google reviews, App Store, social media, support tickets, surveys)" | Must be provided |
| Volume | "Roughly how many pieces of feedback to analyze?" | 50-200 |
| Time period | "What date range?" | Last 90 days |
| Focus | "What do you want to understand? (overall sentiment, specific product, competitor comparison)" | Overall brand sentiment |
| Segments | "Any segments to analyze separately? (product line, customer tier, channel)" | Overall first |
| Existing tracking | "Do you have any sentiment tracking in place?" | None |
GATE: Confirm brief before proceeding.
Phase 2: Analyze
Sentiment Scoring Framework
Classify each piece of feedback:
- Positive — praise, satisfaction, recommendation
- Neutral — factual, no strong emotion, mixed
- Negative — complaint, frustration, warning to others
Theme Categorization
Tag every piece of feedback with 1-2 themes:
- Product quality, pricing, customer service, delivery/speed, usability, feature requests, competitor mentions, billing issues
Analysis Dimensions
- Overall sentiment distribution — % positive, neutral, negative
- Sentiment by theme — which topics generate the most negativity?
- Sentiment trend — is sentiment improving or declining over time?
- Volume trend — are more people talking? (volume increase + negative sentiment = alarm)
- Competitive mentions — how often do customers mention competitors and in what context?
GATE: Present preliminary findings and confirm focus areas for the full report.
Phase 3: Build
Deliverables
1. Sentiment Analysis Report
- Overall sentiment score and distribution
- Theme-by-theme sentiment breakdown
- Trend chart over the analysis period
- Top 10 representative quotes (positive and negative)
- Competitive mention summary
2. Issue Priority Matrix
| Theme | Sentiment | Volume | Trend | Priority |
|---|
| Customer service | Negative | High | Worsening | Critical |
| Product quality | Positive | High | Stable | Protect |
| Pricing | Mixed | Medium | Stable | Monitor |
3. Alert Framework
Define triggers for ongoing monitoring:
- Negative sentiment exceeds 30% in any week
- New negative theme appears that was not previously tracked
- Star rating drops below 4.0 on any review platform
- Competitor mentioned positively more than 10% of the time
4. Response Playbook
- Template responses for common negative themes
- Escalation criteria for serious complaints
- Proactive outreach triggers for at-risk customers
Phase 4: Polish
Monitoring Dashboard Spec
Recommend a simple tracking system:
- Weekly sentiment score by source
- Theme trend tracking (monthly)
- Alert log for triggered notifications
Quarterly Sentiment Review
Template for a quarterly deep-dive comparing current sentiment to previous quarter with action plan updates.
Example 1: App Store Reviews (150 reviews, SaaS mobile app)
Finding: 72% positive, 18% negative, 10% neutral. Top negative theme: onboarding confusion (8 mentions). Top positive: time-saving features.
Action: Improve onboarding flow, create tutorial videos.
Example 2: Google Reviews (80 reviews, local service business)
Finding: 4.2 average stars. Negative reviews cluster around wait times (6 mentions) and billing clarity (4 mentions). Positive reviews highlight staff quality.
Action: Address wait time communication, simplify billing invoices.
Anti-Patterns
- Ignoring negative feedback — 1 negative review often represents 10 silent churners. Take complaints seriously.
- Counting stars without reading text — a 3-star review with specific feedback is more useful than a 5-star "Great!" Read the content.
- Analyzing once and forgetting — sentiment shifts over time. Set up ongoing monitoring, not one-time reports.
- Responding defensively — negative sentiment analysis should drive improvement, not defensive PR responses.
- Overreacting to one bad review — look for patterns. One complaint is an anecdote; five on the same topic is a trend.
Recovery
- Very few reviews to analyze: Supplement with support ticket data, social media comments, or direct customer interviews.
- Overwhelmingly positive (suspicious): Dig deeper — are reviews incentivized? Check for patterns suggesting fake reviews.
- User takes negative feedback personally: Reframe as a competitive advantage — you now know exactly what to fix. Competitors are guessing.
- No ongoing monitoring resources: Set up Google Alerts and a monthly 30-minute review habit. Low-effort monitoring beats no monitoring.
