mcpbeat

Sentiment Analyzer

guia-matthieu/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

3k tokens
context cost
the whole folder, loaded on every use
3
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
145
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill sentiment-analyzer

What comes with it

9 928 bytes besides the instruction
scripts/main.py
scripts/requirements.txt

The instruction itself

17 sections, as written by the author

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

How to use it

Copy the folder

Take guia-matthieu/sentiment-analyzer from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.