The usual question is "what has this business been told?". This asks the other one: "what has this person been saying?". Point it at a Google Maps contributor profile and get their whole public review history back as rows - what they reviewed, how they rated it and when - so a pattern you suspected becomes something you can actually look at.
The lookup runs profile-first rather than place-first. Instead of asking a listing for its reviews, it asks a contributor for theirs.
One row per review, in the same schema a place-based review pull returns - so it drops into whatever you already built on that output.
A single one-star tells you very little. That same author's full history - what else they reviewed, how they rate everything, over what span - tells you whether to take it seriously.
Reviews are normally trapped one listing at a time. Pulled by profile, a contributor's activity arrives as a single continuous list you can sort and count.
Rows come back in the standard review shape, so existing sheets, dashboards and sentiment models keep working without a new mapping.
One row for every review the profile has published. This is its own compact shape - not the wider record the Google Reviews Scraper returns, so don't plan on those column names here.
8 columns per review - every column above on every plan, including the free tier.
Two of these do more work than they look. date is what turns a list into evidence - several one-star reviews of unrelated businesses on the same day is a pattern a single review never shows. And owner_response gives you the other side of each exchange without a second lookup, which matters when you're judging whether a complaint was handled or ignored.
A few examples of how teams use a profile-first review pull.
A damaging review lands and something feels off. Pulling that profile's history shows whether they review widely and fairly or have left a trail of one-stars across a single sector - the evidence you need before disputing it with Google.
Some contributors review hundreds of places in a city. Their history is a ready-made survey of that market - what they visited, in what order, and how they scored it, all from one profile.
If your team reviews suppliers or venues from a shared account, exporting that profile's history gives you the record in a spreadsheet rather than scrolling the app one entry at a time.
No subscription, no minimum. Your first 500 reviews are on us - after that, pay-as-you-go.
Every new account, one-time. No credit card required. All scrapers unlocked, full feature set.
Roughly $2 per 1,000 reviews. The pre-flight estimator shows the cost of a run before you start it - no surprises, no compute units to translate.
Volume pricing, SLAs, dedicated workers, and bespoke onboarding for ongoing or very large pulls. Tell us about your numbers and we'll quote.
LIVESCRAPER10 at checkout.Most teams use this next to one of the place-based review tools.
The place-first direction: every review on a listing you care about. Its data dictionary is also the canonical field reference for the rows this tool returns.
Watch your listings on a schedule and get emailed the new reviews. When one looks questionable, come back here and check who wrote it.
Short answer: yes for the reviews themselves, which the author published publicly - but this one deserves more care than a place-based pull.
A contributor profile page is public: Google shows anyone the reviews a person chose to publish under their display name. Reading that page is no different in kind from reading a listing. As long as the data is publicly available and the process doesn't disrupt the site, there are no federal laws prohibiting it.
The honest caveat is that this output is organised around a person rather than a business, so it is closer to personal data than most of our tools. Under GDPR that matters: you need a lawful basis for processing it, and "we were curious" is not one. Investigating suspected review abuse against your own listing is a defensible reason. Building a profile of an individual is not. We don't police your use, but we'd rather say this plainly than let the page imply otherwise.
We collect nothing the author hasn't published, run no third-party trackers on the data layer, and your exports auto-delete after 30 days.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
contributor profile you asked about, the place_name reviewed, the rating given, the date posted, the review text, any owner_response, the likes count and the language. It's a more compact shape than the Google Reviews Scraper returns - see the data dictionary above for what each one holds.author_link column - you can feed those values straight back in.500 one-time free reviews on every new account - no expiry. After that it's $0.002 per review, pay-as-you-go - no card on file until you say so.
Livescraper's Google Maps Contributor Reviews Scraper reads reviews from the author's end rather than the listing's. You supply a contributor profile and get back the reviews that person has published - what they reviewed, the rating they gave, the text they wrote and when they posted it - as structured rows instead of an endless scroll.
Each row uses the standard Google review schema, the same one our place-based Google Reviews Scraper returns, because it is the same underlying review. That means the author columns, the review text, the rating, the timestamps and the owner replies all arrive in a shape your existing sheets and sentiment models already understand, with no new field mapping to build.
Reputation teams use it when a damaging review looks questionable: an author's wider history shows whether they review broadly and fairly or have left a run of one-stars across one sector, which is the evidence Google asks for in a dispute. Research teams use prolific local guides as a ready-made survey of a city. Operations teams export their own shared account's contributions for the record.
Because the output is organised around a person rather than a business, it sits closer to personal data than most extraction work - you should have a lawful basis for processing it, and we say so plainly on this page rather than leaving it implied. Start free: your first 500 reviews cost nothing and need no credit card.