Review Summarizer

Cleanup Review

python scripts/_reset_state.py is an optional cleanup helper for the Review Summarizer skill. It may recursively remove /tmp/_skill_run/*, /tmp/_skill_run/.cache. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.

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
✅ **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.