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

CODEBLOCK0

Compare Reviews Across Platforms

CODEBLOCK1

Generate Quick Summary

CODEBLOCK2

Scripts

scrape_reviews.py

Parameters:

• - --url: Product or business review URL (required)
• INLINECODE2: Platform (amazon, google, yelp, tripadvisor) (auto-detected if omitted)
• INLINECODE3: Maximum reviews to fetch (default: 100)
• INLINECODE4: Filter to verified purchases only
• INLINECODE5: Minimum rating to include (1-5)
• INLINECODE6: Time filter (7d, 30d, 90d, all) (default: all)
• INLINECODE7: Output file (default: summary.md)
• INLINECODE8: Output format (markdown, json, csv)

Example:
CODEBLOCK3

compare_reviews.py

Parameters:

• - --product: Product name or keyword (required)
• INLINECODE11: Comma-separated platforms (default: all)
• INLINECODE12: Max reviews per platform (default: 50)
• INLINECODE13: Output file
• INLINECODE14: Output format (markdown, json)

Example:
CODEBLOCK4

sentiment_analysis.py

Parameters:

• - --input: Input file or text (required)
• INLINECODE17: Input type (file, text, url)
• INLINECODE18: Analyze specific aspects (comma-separated)
• INLINECODE19: Output file

Example:
CODEBLOCK5

quick_summary.py

Parameters:

• - --url: Review URL (required)
• INLINECODE22: Brief summary only (no detailed breakdown)
• INLINECODE23: Summary word count (default: 150)
• INLINECODE24: Output file

Example:
CODEBLOCK6

export_data.py

Parameters:

• - --input: Summary file or JSON data (required)
• INLINECODE27: Export format (csv, json, excel)
• INLINECODE28: Output file

Example:
CODEBLOCK7

Output Format

Markdown Summary Structure

CODEBLOCK8

Best Practices

For Arbitrage Research

1. 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. 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. 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

With Content Recycler

Automation

Weekly Review Monitoring

CODEBLOCK11

Alert on Negative Trends

CODEBLOCK12

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