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sentiment-analysis

sentiment-analysis

github.com/gtmagentsMarketing & growth43068 installsUpdated 30w ago
Repository

About sentiment-analysis

A skill for teams monitoring community sentiment around launches, incidents, and roadmap changes. It organizes signals, tracks themes over time, scores risks, and connects findings to communications or product actions.

Key features

  1. 01Collect signals from forums, chats, surveys, and support
  2. 02Tag emotion, topic, product area, persona, and severity
  3. 03Track trends alongside launches or incidents
  4. 04Score risks using escalation thresholds
  5. 05Turn findings into communications or product tasks

Use cases

  1. 01Monitor community response to a product launch
  2. 02Assess sentiment shifts during an incident
  3. 03Prepare executive updates with member perspectives
  4. 04Prioritize responses to emerging concerns

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gtmagents/gtm-agents --skill sentiment-analysis

After installing, type/sentiment-analysisin Claude Code to confirm it loaded.

Details

From the project README

Community Sentiment Analysis Skill

When to Use

  • Monitoring community tone during launches, incidents, or roadmap changes.
  • Preparing executive updates that require member sentiment context.
  • Prioritizing response or enablement efforts based on emerging themes.

Framework

  1. Signal Sources – forums, chat transcripts, surveys, social listening, support tickets.
  2. Tagging Schema – categorize by emotion, topic, product area, persona, and severity.
  3. Trend Analysis – track frequency over time, correlate with launches or incidents.
  4. Risk Scoring – define thresholds for escalation (negative volume, influencer involvement, compliance).
  5. Action Loop – translate findings into comms responses, content updates, or product feedback tasks.

Templates

  • Sentiment tagging sheet (message → tags → severity → owner).
  • Trend report layout with charts + narrative.
  • Escalation matrix referencing response SLAs.

Tips

  • Combine manual review with NLP dashboards to balance accuracy + scale.
  • Capture representative quotes for exec storytelling.
  • Pair with measure-engagement command to provide recommendations alongside metrics.

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