review-summarizer（评论总结者）

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.