Clawfu-skills 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

install

source · Clone the upstream repo

git clone https://github.com/guia-matthieu/clawfu-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/guia-matthieu/clawfu-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/analytics/sentiment-analyzer" ~/.claude/skills/guia-matthieu-clawfu-skills-sentiment-analyzer && rm -rf "$T"

manifest: skills/analytics/sentiment-analyzer/SKILL.md

source content

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 - Get social data to analyze
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