How to Scrape Google Maps Reviews: Complete Guide [2026]

Last updated: June 2026

Google Maps reviews are one of the richest sources of unfiltered customer feedback available anywhere. Unlike surveys or focus groups, reviews are written voluntarily, in natural language, by people who have actually interacted with a business. That makes them unusually useful for competitive analysis, sentiment research, and understanding what customers in a given market actually care about.

The challenge is scale. Reading reviews one business at a time is practical for a handful of competitors. When you want to analyze 200 restaurants across a city, or track sentiment trends for an entire category over time, you need a way to extract that data systematically. This guide covers what review data is available, how to extract it, three different methods for doing so, and the legal considerations you should be aware of before you start.

What Data Can You Extract from Google Maps Reviews?

A Google Maps review contains more structured information than the star and text most people notice. Here is the full picture of what can be extracted from a review listing.

• Review text and star rating. The core content — the written review and the 1–5 star score the reviewer assigned.
• Reviewer name and photo. The display name and profile image of the person who left the review, both publicly visible.
• Date published. When the review was posted, essential for tracking sentiment trends over time.
• Owner response. If the business replied to the review, that response text is also extractable — useful for auditing how competitors handle negative feedback.
• Detailed sub-ratings. For restaurants and some other categories, Google shows breakdowns by dimension — Food, Service, Atmosphere — as separate 1–5 scores within the same review.
• Review tags. Google aggregates the most frequently mentioned topics across all reviews for a business into tags like "friendly staff," "wait time," or "good for kids," with a count of how many reviews mention each. These offer a high-level summary without reading every review.
• Reviewer profile data. The reviewer's total review count and Local Guide status are visible on their public profile. Reviewers with many reviews and Local Guide status tend to write more detailed, reliable assessments.

Not all of this data is available for every business. Detailed sub-ratings only appear for certain categories, and review tags require a sufficient number of reviews before Google generates them. A business with fewer than 20 reviews typically will not have tags at all.

5 Ways Businesses Use Google Maps Review Data

1. Competitive Analysis

Pulling average ratings and review counts for every competitor in your market gives you an objective performance benchmark. If you are a dentist in Phoenix with a 4.3 rating and you discover that the top five competitors all have 4.7 or higher, that is a clear signal. You can then read their reviews to understand what they are doing better, without any guesswork.

2. Sentiment Analysis

When you have review text at scale, you can identify what customers consistently praise and what they consistently complain about — for your business and your competitors. A restaurant that keeps getting five-star reviews mentioning "the lamb" and one-star reviews mentioning "parking" has clear signals about where to invest and where a complaint pattern exists. Review tags give you a shortcut to this analysis without reading thousands of individual reviews.

3. Reputation Monitoring

Extracting reviews on a schedule lets you track how ratings shift over time. A business that was at 4.6 six months ago and is now at 4.1 has a trajectory worth understanding. Monitoring your own listing and key competitors on a monthly basis can surface problems early — before a declining rating starts costing you customers.

4. Market Research in a New Market

Before entering a new city or segment, review data tells you what customers in that market expect and where incumbents are failing to deliver. A franchise expanding into a new metro can extract all competitors in a category, read their reviews, and identify the gaps in the market — specific complaints that appear repeatedly across multiple businesses in the same vertical.

5. Content Marketing and Ad Copy

Customer reviews contain the exact language your target audience uses to describe the value they want. When you find that dozens of five-star reviews for a competitor's HVAC service mention "showed up on time" and "explained everything clearly," that is not just a compliment — it is validated messaging. Marketing teams use review language to write landing page copy, ad headlines, and email subject lines that resonate because they are written in customers' own words, not marketing speak.

How to Scrape Google Maps Reviews: 3 Methods

Method 1: Manual Copy-Paste (Small Scale)

For fewer than 10 businesses with a small number of reviews, manually copying review text into a spreadsheet is entirely workable. Open the business listing, click the reviews tab, scroll through the results, and paste what you need. This produces no structure unless you impose it, and it does not scale — but for a quick one-off competitor check it is faster than setting up any tool.

The ceiling is practical: once you are looking at more than a few hundred reviews across multiple businesses, manual copy-paste takes more time than the analysis is worth. At that point a tool becomes necessary.

Method 2: Dedicated Review Scraping Tools

Several tools are built specifically to extract individual reviews at scale. Apify's Google Maps Scraper is the most capable — it accepts a list of Google Maps URLs or place IDs, extracts every review for each business (including reviewer name, date, rating, and text), and exports to CSV or JSON. For a detailed comparison of dedicated tools, the best Google Maps scrapers comparison covers pricing and feature differences across the main options.

The tradeoff is cost and setup. Dedicated review scrapers typically charge per review or per place, and the cost adds up quickly when you are extracting thousands of reviews. Apify charges based on compute units; for a business with 500 reviews you can expect to pay a few dollars per extraction. If you are doing this across 200 businesses, the cost scales accordingly. These tools also require you to already know which specific businesses you want to analyze — they need a URL or place ID as input, not a search query.

Method 3: Start with Business Data, Then Dive into Reviews

The most practical workflow for most use cases is a two-step approach. First, use a business data scraper like TheMapScraper to identify all relevant businesses in your target area — you get a full list with review counts, average ratings, and review tags for every business in one search. Then, once you have identified which specific businesses are worth deeper analysis, feed those listings into a dedicated review extraction tool.

This avoids paying to scrape thousands of reviews from businesses that turn out to be irrelevant. The review overview data — counts, ratings, tags — is often enough to answer the initial question. You only need to go deeper on the handful of businesses that the overview flags as worth investigating. For more on the general Google Maps scraping workflow, that guide covers the full end-to-end process.

What TheMapScraper Extracts for Review Analysis

TheMapScraper is built for extracting business contact data from Google Maps searches, not for extracting individual review text at scale. That distinction matters for setting the right expectations. Here is what you do get that is directly useful for review analysis.

For every business in your search results, TheMapScraper returns the total review count and average star rating. This is enough to rank businesses by reputation and identify outliers — unusually high or low performers in a category — across hundreds of listings in a single search.

It also extracts review tags where available. These are the aggregated topic labels Google generates from the full body of reviews — "friendly staff: 201 mentions," "wait time: 89 mentions," and so on. Review tags give you a fast read on what customers are talking about for each business without reading individual reviews. A business with "long wait: 150 mentions" alongside a 3.8 rating tells you a lot without opening a single review.

This gives you a review overview across hundreds of businesses simultaneously. For identifying which businesses to analyze further, and for market-level reputation benchmarking, this data is often sufficient. All of it exports to a clean CSV you can open in Excel or Google Sheets and filter immediately.

For individual review text — the full content of each review written by each customer — you will need a dedicated review scraper running against the specific businesses you identify in step one.

Legal Considerations for Scraping Google Maps Reviews

Google Maps reviews are publicly accessible — anyone can read them without logging in. US case law, including the hiQ v. LinkedIn ruling, has established meaningful precedent supporting the right to scrape publicly available data. That said, the legal picture varies by jurisdiction and continues to evolve, so nothing here should be taken as legal advice.

A few practical guidelines reduce your exposure regardless of jurisdiction. Do not republish scraped reviews as your own content or pass them off as original testimonials — that is both legally questionable and a violation of Google's Terms of Service. Use review data for analysis and internal decision-making, not for mass redistribution.

In the European Union, GDPR applies to personal data. Reviewer names and profile information are technically personal data. For business analysis purposes this is generally a low-risk use, but if you are building a product or database on top of review data and operating in the EU, consult a privacy specialist before scaling.

Technically scraping Google Maps does violate Google's Terms of Service, which is a civil matter rather than a criminal one. The practical consequence is IP blocking or rate limiting from Google's side rather than legal action. Using tools that respect reasonable request rates, rather than hammering Google's servers, significantly reduces this risk.

Frequently Asked Questions

Google Maps reviews are one of the best available sources of unfiltered, authentic customer feedback. Getting that data into a structured format opens up analysis that simply is not possible by reading listings one at a time. The most efficient path for most teams is to start with a business data scraper to identify the landscape, then use a dedicated review tool on the specific businesses that are worth going deeper on.

TheMapScraper is a good starting point for the first step: run a search and get review counts, ratings, and tags for every business in your target market in under three minutes. The first 50 leads are free with no credit card required.