
Sentiment Analyzer
This one's for processing customer feedback at scale without burning hours on manual categorization. It runs transformer models locally to analyze sentiment in reviews, NPS responses, or support tickets, giving you scored outputs from very positive to very negative. The batch processing handles CSVs and generates HTML reports with breakdown summaries. ClawFu built it with both heavy ML and lighter CPU options depending on your setup. Honestly most useful when you've got hundreds of text entries to sort through and need to spot patterns fast. The categorization table showing score ranges is helpful for setting up automations or dashboards based on sentiment thresholds.
- skills.sh installs
- 221
- GitHub stars
- 150
- last updated
- Aug 21, 2026
Install
$ npx -y skills add guia-matthieu/clawfu-skills --skill sentiment-analyzer --agent claude-codeInstalls into .claude/skills of the current project.
Files
Sentiment Analyzer
Analyze sentiment in customer feedback using transformer models - understand what your customers really feel at scale.
When to Use This Skill
- Review analysis - Process hundreds of product reviews
- NPS feedback - Categorize open-ended survey responses
- Social listening - Monitor brand sentiment on social media
- Campaign feedback - Evaluate response to marketing campaigns
- Support insights - Categorize support ticket sentiment
What Claude Does vs What You Decide
| Claude Does | You Decide |
|---|---|
| Structures analysis frameworks | Metric definitions |
| Identifies patterns in data | Business interpretation |
| Creates visualization templates | Dashboard design |
| Suggests optimization areas | Action priorities |
| Calculates statistical measures | Decision thresholds |
Dependencies
pip install transformers torch pandas click
# Or for lighter CPU-only version:
pip install textblob vaderSentiment pandas click
Commands
Sponsors
Details
- Repository
- github.com/guia-matthieu/clawfu-skills
- Categories
- DevOps & CI/CD, Data Science & ML, Marketing & SEO
- First seen
- Jun 3, 2026
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