What this robot does
Fiverr shows you a seller's overall rating, but the real story is buried in the individual reviews. A 4.8-star seller might have glowing feedback for simple projects and consistent complaints about communication on complex ones. The only way to know is to read the reviews - and on popular gigs, that means scrolling through hundreds of entries.
This Fiverr reviews scraper extracts every review from a gig page: the reviewer's name, country, star rating, full review text, and posting date. With the data in a spreadsheet, you can search for keywords like 'late' or 'revision,' sort by rating to find the critical feedback fast, and compare review patterns across multiple sellers. Whether you are a business vetting freelancers for an important project, a recruiter evaluating talent, or a Fiverr seller analyzing your own feedback, this robot replaces hours of scrolling with structured, searchable data.
Why data-driven freelancer vetting beats scrolling through reviews:
- ✓ Every review from a Fiverr gig captured in a spreadsheet - star ratings, full text, delivery feedback, and dates - so you can search, sort, and filter instead of scroll.
- ✓ Pattern detection across hundreds of reviews: search for keywords like 'revision,' 'late,' or 'communication' to spot recurring issues that a surface-level rating hides.
- ✓ Multi-seller comparison by extracting reviews from several competing gigs and analyzing them side by side in one dataset - ratings, response themes, and delivery consistency.
- ✓ Seller self-analysis for Fiverr freelancers who want to understand their own feedback trends, identify areas for improvement, and track review sentiment over time.
| Position | Reviewer Username | Reviewer Country | Review Date (ago) | Review Rate | Review Text | Reviewer Avatar |
|---|
| #1 | sarah_designs | United States | 2 weeks ago | 5 | Amazing work! Delivered early and exactly what I needed. | thumbnail |
| #2 | mark_content | United Kingdom | 1 month ago | 4 | Good quality but took longer than expected to respond. | thumbnail |
| #3 | jenny_mktg | Canada | 3 weeks ago | 5 | Professional, thorough, and very communicative throughout. | thumbnail |
| #4 | david_startup | Australia | 2 months ago | 3 | Decent work overall, but revisions were slow. | thumbnail |
| #5 | priya_dev | India | 1 week ago | 5 | Excellent attention to detail. Will definitely hire again. | thumbnail |
No Fiverr API access, no web scraping scripts, and nothing to install. Copy a gig URL and the robot collects every review.
- A free Browse AI account (no credit card required).
- The URL of the Fiverr gig page whose reviews you want to extract.
1
Sign up for free
Create your Browse AI account in under a minute. No credit card required. You will find this prebuilt robot in the robot library ready to use.
2
Paste the Fiverr gig URL
Find the Fiverr gig you want to evaluate and copy the page URL. Paste it into the robot. To compare multiple freelancers, queue several gig URLs and run them together - the reviews from each gig will be extracted into separate datasets you can analyze side by side.
3
Run the robot
Click run. The robot loads the Fiverr gig page, scrolls through the entire review section, and captures every review: the reviewer's username, country, star rating, full review text, and posting date. Gigs with hundreds of reviews are handled automatically.
4
Connect integrations or export your data
Your review data is ready to analyze. Export to Google Sheets to sort by rating and search for keywords, sync to Airtable for a structured freelancer evaluation database, or connect through Zapier to feed review data into your procurement or vendor management workflow.
What can you do with Fiverr gig review data?
Review-level data reveals what overall ratings cannot - here is how teams use extracted Fiverr feedback:
- Freelancer due diligence: Before hiring, extract all reviews for a gig and search for red flags - mentions of missed deadlines, poor communication, or excessive revisions that a 4.7-star average obscures.
- Multi-seller benchmarking: Extract reviews from three or four competing gigs in the same category. Compare average ratings, delivery consistency, and the tone of recent feedback to pick the best option.
- Procurement documentation: Save review data as part of your vendor evaluation records. When stakeholders ask why you chose a particular freelancer, share the structured evidence.
- Seller reputation monitoring: Fiverr sellers can extract their own gig reviews to track sentiment trends, identify recurring complaints, and measure whether service improvements are reflected in newer feedback.
- Review keyword analysis: Search extracted review text for specific terms - 'quality,' 'fast,' 'revisions,' 'responsive' - to quantify what buyers value most about a seller's work.
- Category-wide quality benchmarking: Extract reviews from multiple top-rated gigs in a Fiverr category to establish a baseline for what 'good' looks like - average delivery times, common praise themes, and typical complaint patterns.
🔍
Procurement and operations teams
Vet Fiverr freelancers with data. Extract gig reviews and analyze delivery patterns, rating distributions, and buyer sentiment before committing to a hire.
📊
Fiverr sellers and freelancers
Understand your own review landscape. Extract your gig reviews to identify feedback trends, spot areas for improvement, and track how changes to your service affect buyer satisfaction.
🏢
Agencies sourcing freelance talent
Build a vetted freelancer roster by systematically extracting and comparing reviews across multiple Fiverr gigs in each service category you outsource.
📈
Market researchers and analysts
Study the Fiverr marketplace at the review level. Analyze what drives high ratings in specific categories and how buyer expectations differ across service types.
Each review on the gig page becomes a structured row with these fields:
| Field | What it contains |
|---|
| Position | The order position of the review on the gig page. |
| Reviewer Username | The Fiverr username of the person who left the review. |
| Reviewer Country | The country of the reviewer, if displayed on the gig page. |
| Review Date (ago) | When the review was posted, displayed as time elapsed. |
| Review Rate | The numerical or star rating given by the reviewer. |
| Review Text | The full text of the review comment. |
| Reviewer Avatar | The profile avatar image of the reviewer. |
Fiverr displays reviews in its default sorting order. The robot captures them as shown on the page. Run extractions on different dates to track how new reviews change the overall sentiment for a gig.
Frequently asked questions
Can I extract reviews from any Fiverr gig?
Yes. The robot works on any publicly accessible Fiverr gig page that has reviews. Paste the gig URL and the robot scrolls through and captures every review displayed.
How many reviews can the robot extract from one gig?
The robot captures all reviews displayed on the gig page. Popular gigs with hundreds of reviews are handled automatically - the robot scrolls through the entire review section.
Can I compare reviews across multiple Fiverr sellers?
Yes. Run the robot on multiple gig URLs and export the results. You can combine datasets and compare sellers side by side in one spreadsheet.
Reviews are the deepest layer of freelancer evaluation - combine them with search and category data for a complete Fiverr research workflow:
- Fiverr category gigs scraper - Find the top gigs in a Fiverr category first, then extract reviews from the most promising sellers to make a data-driven hiring decision.
- Fiverr Pros search scraper - Research Fiverr Pro sellers by keyword. Extract the search results to find high-end freelancers, then use the reviews scraper to validate their reputation.
- Fiverr search results scraper - Cast a wider net by extracting seller listings from Fiverr search results. Identify candidates first, then drill into their gig reviews for detailed vetting.
Vet Fiverr freelancers with review data, not guesswork
Ratings, review text, delivery feedback, dates - every review from any gig page in a searchable spreadsheet.
