sentiment-analyzer
Analyze sentiment in text using ML models. Use when: analyzing customer reviews; processing NPS feedback; monitoring brand mentions; evaluating campaign responses; categorizing support tickets
适合你，如果需要从大量文本中快速识别用户情绪和态度
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
~/.claude/skills/(项目级 .claude/skills/)~/.codex/skills/npx oh-my-skill add guia-matthieu/clawfu-skills/sentiment-analyzercurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- guia-matthieu/clawfu-skills/sentiment-analyzernpx oh-my-skill verify guia-matthieu/clawfu-skills/sentiment-analyzer怎么用
技能原文 SKILL.md
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
Analyze Text
python scripts/main.py analyze "This product exceeded my expectations!" python scripts/main.py analyze "The service was terrible and slow."
Batch Analysis
python scripts/main.py batch reviews.csv --column text python scripts/main.py batch feedback.csv --column comment --output results.csv
Generate Report
python scripts/main.py report reviews.csv --column text --output sentiment-report.html
Examples
Example 1: Analyze Product Reviews
# Process CSV of reviews python scripts/main.py batch amazon-reviews.csv --column review_text # Output: amazon-reviews_sentiment.csv # review_text | sentiment | score | label # "Absolutely love this!" | positive | 0.95 | Very Positive # "It's okay, nothing special" | neutral | 0.52 | Neutral # "Worst purchase ever" | negative | 0.12 | Very Negative
Example 2: NPS Feedback Categorization
# Analyze NPS survey responses python scripts/main.py report nps-responses.csv --column feedback # Output: sentiment-report.html # Summary: # - Positive: 62% (mainly: product quality, support) # - Neutral: 23% (mainly: pricing concerns) # - Negative: 15% (mainly: shipping delays)
Sentiment Categories
| Score Range | Label | Interpretation | |-------------|-------|----------------| | 0.8 - 1.0 | Very Positive | Enthusiastic, recommend | | 0.6 - 0.8 | Positive | Satisfied, happy | | 0.4 - 0.6 | Neutral | Mixed or indifferent | | 0.2 - 0.4 | Negative | Disappointed, frustrated | | 0.0 - 0.2 | Very Negative | Angry, will churn |
Skill Boundaries
What This Skill Does Well
- Structuring data analysis
- Identifying patterns and trends
- Creating visualization frameworks
- Calculating statistical measures
What This Skill Cannot Do
- Access your actual data
- Replace statistical expertise
- Make business decisions
- Guarantee prediction accuracy
Related Skills
- [social-analytics](../../social/social-analytics/) - Get social data to analyze
- [content-repurposer](../../automation/content-repurposer/) - Use insights for content
Skill Metadata
- Mode: centaur
category: analytics subcategory: nlp dependencies: [transformers, torch, pandas] difficulty: intermediate time_saved: 6+ hours/week