/sentiment-analyzer

name: sentiment-analyzer
description: "Analyze sentiment in text using ML models. Use when: analyzing customer reviews; processing NPS feedback; monitoring brand mentions; evaluating campaign responses; categorizing support tickets"
license: MIT
metadata:
  author: ClawFu
  version: 1.0.0
  mcp-server: "@clawfu/mcp-skills"

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