Etsy Review Scraper

The Etsy Review Scraper helps you collect detailed customer feedback, product insights, and rating data from any Etsy shop. It transforms public review information into structured, analysis-ready datasets for research, performance tracking, and product optimization.

This tool streamlines the process of gathering high-value review data, enabling businesses and analysts to better understand customer sentiment and product trends.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

The Etsy Review Scraper extracts structured review information based on an Etsy shop name keyword. It gathers key buyer signals such as ratings, review text, product titles, and response messages.

It's ideal for:

• Shop owners evaluating their performance
• Researchers studying marketplace behavior
• Analysts identifying customer preferences
• Marketers gathering social proof

Why Automated Review Extraction Matters

• Removes the need for manual review collection
• Enables consistent and scalable data gathering
• Provides structured insights for reporting and analytics
• Helps identify customer patterns and product opportunities

Features

Feature	Description
Comprehensive Review Capture	Collects full review text, ratings, product details, and responses.
Flexible Input Targeting	Use keywords to specify which shop’s reviews to extract.
Multi-format Output	Export data in JSON, CSV, Excel, HTML, or XML formats.
Automated Validation	Ensures accurate extraction of review and product fields.
Scalable Processing	Handles large datasets with efficient performance.

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What Data This Scraper Extracts

Field Name	Field Description
receipt_id	Unique transaction identifier for the review.
buyer_login_name	Username of the reviewer.
buyer_real_name	Full name of the reviewer, when available.
date	Date when the review was posted.
listing_title	Full product title associated with the review.
listing_image_url	URL of the product image.
listing_review	Customer’s written review.
response	Seller’s response to the review, if any.
rating	Numerical rating score (1–5 stars).

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Example Output

[
  {
    "receipt_id": 3466645038,
    "buyer_login_name": "vickiedwards7",
    "listing_title": "Melody Chimes...",
    "listing_review": "Beautiful sound...",
    "response": null,
    "rating": 5
  }
]

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Directory Structure Tree

Etsy Review Scraper/
├── src/
│   ├── runner.py
│   ├── extractors/
│   │   ├── etsy_review_parser.py
│   │   └── utils_date.py
│   ├── outputs/
│   │   └── exporters.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── input.sample.json
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• Ecommerce sellers analyze customer satisfaction to identify improvement areas and strengthen product offerings.
• Market researchers gather competitor review data to track pricing sentiment and consumer expectations.
• Product teams evaluate recurring feedback patterns to guide product development decisions.
• Brand managers monitor reputation trends to improve customer engagement and response strategies.
• Marketing teams extract customer testimonials to enhance social proof and conversion campaigns.

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FAQs

Q: Do I need a shop's exact name to extract reviews? A: No. You can use a keyword that closely matches the shop name, and the scraper will target the correct store.

Q: What output formats are supported? A: The scraper can generate JSON, CSV, Excel, HTML, and XML files for flexible use across tools.

Q: Can this scraper collect seller responses as well? A: Yes, if a seller has replied to a review, the response is included in the output.

Q: How many reviews can I extract at once? A: You can configure the extraction limit based on your needs by adjusting the limit parameter.

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Performance Benchmarks and Results

Primary Metric: Capable of processing dozens of review pages in under a minute under standard network conditions. Reliability Metric: Maintains a success rate above 98% due to built-in validation and error recovery. Efficiency Metric: Optimized to minimize redundant requests and reduce processing overhead. Quality Metric: Achieves high data completeness with accurate capture of ratings, text, and metadata.

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