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BytesAgain β€Ί Skills β€Ί Review Summarizer
πŸ¦€ ClawHub

Review Summarizer

by @michael-laffin

Scrape, analyze, and summarize product reviews from multiple platforms (Amazon, Google, Yelp, TripAdvisor). Extract key insights, sentiment analysis, pros/cons, and recommendations. Use when researching products for arbitrage, creating affiliate content, or making purchasing decisions.

Versionv1.0.0
Downloads2,560
Installs8
Stars⭐ 3
#research#writing#ecommerce#legal#travel
TERMINAL
clawhub install review-summarizer

πŸ“– About This Skill

name: review-summarizer description: Scrape, analyze, and summarize product reviews from multiple platforms (Amazon, Google, Yelp, TripAdvisor). Extract key insights, sentiment analysis, pros/cons, and recommendations. Use when researching products for arbitrage, creating affiliate content, or making purchasing decisions.

Review Summarizer

Overview

Automatically scrape and analyze product reviews from multiple platforms to extract actionable insights. Generate comprehensive summaries with sentiment analysis, pros/cons identification, and data-driven recommendations.

Core Capabilities

1. Multi-Platform Review Scraping

Supported Platforms:

  • Amazon (product reviews)
  • Google (Google Maps, Google Shopping)
  • Yelp (business and product reviews)
  • TripAdvisor (hotels, restaurants, attractions)
  • Custom platforms (via URL pattern matching)

    • Scrape Options:

  • All reviews or specific time ranges
  • Verified purchases only
  • Filter by rating (1-5 stars)
  • Include images and media
  • Max review count limits

    • 2. Sentiment Analysis

    Analyzes:

  • Overall sentiment score (-1.0 to +1.0)
  • Sentiment distribution (positive/neutral/negative)
  • Key sentiment drivers (what causes positive/negative reviews)
  • Trend analysis (sentiment over time)
  • Aspect-based sentiment (battery life, quality, shipping, etc.)

    • 3. Insight Extraction

    Automatically identifies:

  • Top pros mentioned in reviews
  • Common complaints and cons
  • Frequently asked questions
  • Use cases and applications
  • Competitive comparisons mentioned
  • Feature-specific feedback

    • 4. Summary Generation

    Output formats:

  • Executive summary (150-200 words)
  • Detailed breakdown by category
  • Pros/cons lists with frequency counts
  • Statistical summary (avg rating, review count, etc.)
  • CSV export for analysis
  • Markdown report for documentation

    • 5. Recommendation Engine

    Generates recommendations based on:

  • Overall sentiment score
  • Review quantity and recency
  • Verified purchase ratio
  • Aspect-based ratings
  • Competitive comparison

    • Quick Start

    Summarize Amazon Product Reviews

    # Use scripts/scrape_reviews.py
python3 scripts/scrape_reviews.py \
  --url "https://amazon.com/product/dp/B0XXXXX" \
  --platform amazon \
  --max-reviews 100 \
  --output amazon_summary.md

    Compare Reviews Across Platforms

    # Use scripts/compare_reviews.py
python3 scripts/compare_reviews.py \
  --product "Sony WH-1000XM5" \
  --platforms amazon,google,yelp \
  --output comparison_report.md

    Generate Quick Summary

    # Use scripts/quick_summary.py
python3 scripts/quick_summary.py \
  --url "https://amazon.com/product/dp/B0XXXXX" \
  --brief \
  --output summary.txt

    Scripts

    scrape_reviews.py

    Scrape and analyze reviews from a single URL.

    Parameters:

  • --url: Product or business review URL (required)
  • --platform: Platform (amazon, google, yelp, tripadvisor) (auto-detected if omitted)
  • --max-reviews: Maximum reviews to fetch (default: 100)
  • --verified-only: Filter to verified purchases only
  • --min-rating: Minimum rating to include (1-5)
  • --time-range: Time filter (7d, 30d, 90d, all) (default: all)
  • --output: Output file (default: summary.md)
  • --format: Output format (markdown, json, csv)

    • Example: 

    python3 scripts/scrape_reviews.py \
  --url "https://amazon.com/dp/B0XXXXX" \
  --platform amazon \
  --max-reviews 200 \
  --verified-only \
  --format markdown \
  --output product_summary.md

    compare_reviews.py

    Compare reviews for a product across multiple platforms.

    Parameters:

  • --product: Product name or keyword (required)
  • --platforms: Comma-separated platforms (default: all)
  • --max-reviews: Max reviews per platform (default: 50)
  • --output: Output file
  • --format: Output format (markdown, json)

    • Example: 

    python3 scripts/compare_reviews.py \
  --product "AirPods Pro 2" \
  --platforms amazon,google,yelp \
  --max-reviews 75 \
  --output comparison.md

    sentiment_analysis.py

    Analyze sentiment of review text.

    Parameters:

  • --input: Input file or text (required)
  • --type: Input type (file, text, url)
  • --aspects: Analyze specific aspects (comma-separated)
  • --output: Output file

    • Example: 

    python3 scripts/sentiment_analysis.py \
  --input reviews.txt \
  --type file \
  --aspects battery,sound,quality \
  --output sentiment_report.md

    quick_summary.py

    Generate a brief executive summary.

    Parameters:

  • --url: Review URL (required)
  • --brief: Brief summary only (no detailed breakdown)
  • --words: Summary word count (default: 150)
  • --output: Output file

    • Example: 

    python3 scripts/quick_summary.py \
  --url "https://yelp.com/biz/example-business" \
  --brief \
  --words 100 \
  --output summary.txt

    export_data.py

    Export review data for further analysis.

    Parameters:

  • --input: Summary file or JSON data (required)
  • --format: Export format (csv, json, excel)
  • --output: Output file

    • Example: 

    python3 scripts/export_data.py \
  --input product_summary.json \
  --format csv \
  --output reviews_data.csv

    Output Format

    Markdown Summary Structure

    # Product Review Summary: [Product Name]
    Overview
    

  • Platform: Amazon
    • 

  • Reviews Analyzed: 247
    • 

  • Average Rating: 4.3/5.0
    • 

  • Overall Sentiment: +0.72 (Positive)
    • Key Insights
    Top Pros
    
1. Excellent sound quality (89 reviews)
2. Great battery life (76 reviews)
3. Comfortable fit (65 reviews)
    Top Cons
    
1. Expensive (34 reviews)
2. Connection issues (22 reviews)
3. Limited color options (18 reviews)
    Sentiment Analysis
    

  • Positive: 78% (193 reviews)
    • 

  • Neutral: 15% (37 reviews)
    • 

  • Negative: 7% (17 reviews)
    • Recommendation
    
βœ… Recommended - Strong positive sentiment with high customer satisfaction.

    Best Practices

    For Arbitrage Research

    1. Compare across platforms - Check Amazon vs eBay seller ratings 2. Look for red flags - High return rates, quality complaints 3. Check authenticity - Verified purchases only 4. Analyze trends - Recent review sentiment vs older reviews

    For Affiliate Content

    1. Extract real quotes - Use actual customer feedback 2. Identify use cases - How people use the product 3. Find pain points - Problems the product solves 4. Build credibility - Use data from many reviews

    For Purchasing Decisions

    1. Check recent reviews - Last 30-90 days 2. Look at 1-star reviews - Understand worst-case scenarios 3. Consider your needs - Match features to your use case 4. Compare alternatives - Use compare_reviews.py

    Integration Opportunities

    With Price Tracker

    Use review summaries to validate arbitrage opportunities: 
    # 1. Find arbitrage opportunity
price-tracker/scripts/compare_prices.py --keyword "Sony WH-1000XM5"
    2. Validate with reviews
    
review-summarizer/scripts/scrape_reviews.py --url [amazon_url]
review-summarizer/scripts/scrape_reviews.py --url [ebay_url]
    3. Make informed decision

    With Content Recycler

    Generate content from review insights: 
    # 1. Summarize reviews
review-summarizer/scripts/scrape_reviews.py --url [amazon_url]
    2. Use insights in article
    
seo-article-gen --keyword "[product name] review" --use-insights review_summary.json
    3. Recycle across platforms
    
content-recycler/scripts/recycle_content.py --input article.md

    Automation

    Weekly Review Monitoring

    # Monitor competitor products
0 9 * * 1 /path/to/review-summarizer/scripts/compare_reviews.py \
  --product "competitor-product" \
  --platforms amazon,google \
  --output /path/to/competitor_analysis.md

    Alert on Negative Trends

    # Check for sentiment drops below threshold
if [ $(grep -o "Sentiment: -" summary.md | wc -l) -gt 0 ]; then
  echo "Negative sentiment alert" | mail -s "Review Alert" user@example.com
fi

    Data Privacy & Ethics

  • Only scrape publicly available reviews
  • Respect robots.txt and rate limits
  • Don't store PII (personal information)
  • Aggregate data, don't expose individual reviewers
  • Follow platform terms of service

    • Limitations

  • Rate limiting on some platforms
  • Cannot access verified purchase status on all platforms
  • Fake reviews may skew analysis
  • Language support varies by platform
  • Some platforms block scraping

    • Make data-driven decisions. Automate research. Scale intelligence.

    πŸ’‘ Examples

    Summarize Amazon Product Reviews

    # Use scripts/scrape_reviews.py
python3 scripts/scrape_reviews.py \
  --url "https://amazon.com/product/dp/B0XXXXX" \
  --platform amazon \
  --max-reviews 100 \
  --output amazon_summary.md

    Compare Reviews Across Platforms

    # Use scripts/compare_reviews.py
python3 scripts/compare_reviews.py \
  --product "Sony WH-1000XM5" \
  --platforms amazon,google,yelp \
  --output comparison_report.md

    Generate Quick Summary

    # Use scripts/quick_summary.py
python3 scripts/quick_summary.py \
  --url "https://amazon.com/product/dp/B0XXXXX" \
  --brief \
  --output summary.txt

    πŸ“‹ Tips & Best Practices

    For Arbitrage Research

    1. Compare across platforms - Check Amazon vs eBay seller ratings 2. Look for red flags - High return rates, quality complaints 3. Check authenticity - Verified purchases only 4. Analyze trends - Recent review sentiment vs older reviews

    For Affiliate Content

    1. Extract real quotes - Use actual customer feedback 2. Identify use cases - How people use the product 3. Find pain points - Problems the product solves 4. Build credibility - Use data from many reviews

    For Purchasing Decisions

    1. Check recent reviews - Last 30-90 days 2. Look at 1-star reviews - Understand worst-case scenarios 3. Consider your needs - Match features to your use case 4. Compare alternatives - Use compare_reviews.py