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
bash
pip install transformers torch pandas click # Or for lighter CPU-only version: pip install textblob vaderSentiment pandas click
Commands
Analyze Text
bash
python scripts/main.py analyze "This product exceeded my expectations!" python scripts/main.py analyze "The service was terrible and slow."
Batch Analysis
bash
python scripts/main.py batch reviews.csv --column text python scripts/main.py batch feedback.csv --column comment --output results.csv
Generate Report
bash
python scripts/main.py report reviews.csv --column text --output sentiment-report.html
Examples
Example 1: Analyze Product Reviews
bash
# 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
bash
# 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 - Get social data to analyze
- •content-repurposer - Use insights for content
Skill Metadata
- •Mode: centaur
yaml
category: analytics subcategory: nlp dependencies: [transformers, torch, pandas] difficulty: intermediate time_saved: 6+ hours/week