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sentiment-analyzer

Marketing Autor: guia-matthieu

Analyze sentiment in text using ML models. Use when: analyzing customer reviews; processing NPS feedback; monitoring brand mentions; evaluating campaign responses; categorizing support tickets

126estrelas
Atualizado há 7 meses
Ver no GitHub ↗ Licença: MIT

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Ler descrição completa

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
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