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