STEP 01
Point at the listings
Give the robot one business listing or a whole list of them. Agent actions open each place page and switch to its reviews tab before it starts reading.
Google Reviews Scraper: Export Star Ratings, Review Text, and Sentiment Into a Spreadsheet
A Google reviews scraper turns the reviews on a business listing into structured rows: reviewer name, star rating, date, full review text, and the owner response. WebRobot does it with an AI agent. Point it at one listing or a hundred, name the fields you want, and it scrolls every review, reads it, and delivers clean, deduplicated rows to a spreadsheet or your reputation dashboard on a schedule.
No CSS selectors to maintain, no extension to babysit. The robot stores the intent, not a brittle path, so the daily pull keeps landing after Google reshuffles its layout.
Last updated July 2026
Robot console · WR-01
Standing by Running · s Complete · rows
1 · Pick a target
3 · Fields to extract
Agent log
Crawl graph
Extracted data · rows
Want this data fresh every morning, without lifting a finger?
01 / FIELDS
FIG. 1 · WHAT COMES OFF A REVIEW
Every field a Google review exposes, in one row
A review is more than a star count. Here is what the robot reads off each one and what teams do with
it.
| Field | What it is | Who uses it |
|---|---|---|
| Star rating | The 1 to 5 rating on the individual review | Tracking the score distribution, not just the rounded average |
| Review text | The full written review, however long | Sentiment analysis, theme extraction, and finding recurring complaints |
| Reviewer and local guide badge | Display name and whether they are a Local Guide | Weighting reviews and spotting suspicious or brigaded activity |
| Review date | When the review was posted | Trending sentiment over time and measuring the effect of a fix |
| Owner response | The business reply and its date | Auditing response rate and speed across locations |
| Aggregate rating and count | The listing average and total number of reviews | Benchmarking a business against nearby competitors |
| Location tag | Which listing the review came from | Multi-location brands: one dashboard, every store split out |
Need the business details alongside the reviews (phone, website, category, hours)? The Google Maps scraper pulls the full listing in the same run. For the category overview, start at the web scraping tool pillar.
02 / TWO ROUTES
FIG. 2 · API OR SCRAPE
Google Places API versus scraping the reviews
Google's Places API returns reviews, but it caps them hard. Knowing where each route wins saves money
and keeps reputation work from stalling on missing data.
| Places API (New) | Scraping the reviews | |
|---|---|---|
| Review depth | Roughly five reviews per place | The full set of visible reviews on the listing |
| History | A rotating recent selection | Everything currently shown, re-read on a schedule to build history |
| Cost model | Per request, billed by field group | Flat monthly plan, no per-review fee |
| Multi-location | One place per call, cost scales with volume | A whole roster of listings in one run |
| Storage | Terms limit how long you may cache reviews | You hold the public data you collect, within the law |
| Best for | A quick, sanctioned rating check | Reputation depth, sentiment, and cross-location tracking |
Many teams use both: the API for a fast rating snapshot, a scraper for the depth reputation work actually needs. The full method, with the rules, is in how to scrape Google reviews. On legality generally, see is web scraping legal.
03 / PROCEDURE
FIG. 3 · SETUP
How to scrape Google reviews in four steps
The first pull takes about ten minutes to set up. Every refresh after that runs itself.
STEP 02
Name the fields
"Reviewer, star rating, date, review text, owner response." The robot reads each one and tags it with its location. No selectors, no code.
STEP 03
Scroll every review
Reviews load as you scroll, so the robot scrolls the panel to the end to capture the full set, not just the first few, and dedupes so each review appears once.
STEP 04
Deliver and schedule
Google Sheets, Excel, Slack, Zapier, or the REST API into your dashboard. Schedule daily and each run flags new reviews so a bad one reaches you fast.
04 / USE CASES
FIG. 4 · WHO RUNS THIS
Where a Google reviews dataset pays for itself
Reputation management
Pull every review across your locations into one sheet, watch the score by branch, and catch a location sliding before it dents revenue. Schedule it daily and a new one-star review reaches the right person within the hour.
Multi-location brands
A franchise or chain with fifty listings gets one dashboard: average rating, review volume, and owner-response rate per store, ranked. The location tag on every row means no manual stitching across spreadsheets.
Local SEO agencies
Show a prospect exactly where their star rating and review velocity sit against the three competitors ranking above them, with real numbers. It is a concrete pitch, and tracking it monthly proves the work is moving the score.
Sentiment and theme analysis
Feed the review text to a language model and pull out the recurring themes: slow service, a product defect, a standout employee. The complaints buried in three hundred reviews become a ranked list of what to fix first.
Competitor teardown
Read the one and two-star reviews on a competitor and you have a map of where they let customers down, in their customers' own words. That is positioning research and product research in the same pull.
Response-rate auditing
Capture the owner response and its date on each review and you can measure how fast and how often each location replies. Pair it with website change monitoring to watch the listing itself for edits.
Pricing is flat: plans on pricing start at $79 per month with no per-review fee, so tracking fifty locations costs the same as tracking five. For a broader listing pull, see the data extraction tool.
05 / FAQ
FIG. 5 · FIELD QUESTIONS
Google reviews scraper questions, answered
A Google reviews scraper is software that reads the reviews on a Google business listing and turns
them into structured rows: reviewer name, star rating, date, full review text, any owner response,
and the aggregate rating and count. Instead of scrolling and copying reviews by hand, an agent
opens the listing, loads every visible review, and delivers a clean spreadsheet or feed that
refreshes on a schedule so you can track sentiment over time.
Point a no-code agent at a business listing or a set of listings, name the fields you want
(reviewer, rating, date, text, owner response), and let it scroll the reviews panel to the end and
read each one. It deduplicates and delivers the rows to Google Sheets, Excel, or your database.
There are no CSS selectors to write and nothing to fix when Google changes its layout, because the
robot stores what you asked for, not a brittle path.
Review text and star ratings are displayed publicly, and US courts have generally treated the
collection of public information as lawful. Two limits apply. Google's terms restrict automated
access, so keep the request rate low and polite. And a reviewer's name attached to their words is
personal data, so if you store it, treat it under CCPA and other privacy rules and do not hoard
identities you have no reason to keep. Aggregate ratings and anonymized sentiment carry the least
risk.
Yes. Give the robot a list of listings, store URLs, or business names and it works through them in
one run, tagging every review with the location it came from. Multi-location brands and agencies
use this to build one sentiment dashboard across every store, compare ratings between branches, and
catch a single location whose score is slipping before it shows up in revenue.
The Places API returns a clean but capped set of reviews, roughly five per place, and limits how
long you may store them. Reach for a scraper when you need the full set of visible reviews,
historical depth, or coverage across many locations the per-call API pricing would make expensive.
Many teams use the API for a quick rating check and a scraper for the depth reputation work
actually needs.
WebRobot is flat-priced: Launch at $79 per month ($63 yearly) covers 5 robots and 10,000 records,
enough to track reviews across a handful of locations. Scale at $249 ($199 yearly) adds hourly
schedules and 100,000 records for a multi-location brand or an agency roster. There is no per-review
fee, so pulling thousands of reviews across many listings does not inflate the bill the way
per-call API pricing does.
Yes. Schedule the robot to re-read your listings daily or hourly and each pass flags reviews that
were not there last time, so a new one-star review reaches your team in Slack or email within the
hour instead of days later. That turns a scrape into a monitor: you respond faster, protect the
rating, and keep a full history of how sentiment moves.
FINAL ASSEMBLY
Every review across every location, in one spreadsheet that updates itself
Point the robot at your listings, name the fields, and get a fresh sentiment dataset on a schedule.