A restaurant review dataset brings ratings and reviews from Google, Yelp, Tripadvisor, and delivery apps into one table. So you can compare every outlet on one scale, instead of checking each site by hand.

Diners spread their attention, too. In fact, the average US consumer uses six review sites, according to BrightLocal’s 2026 survey. Meanwhile, 74% look for reviews written in the last three months.

This guide walks through each step of building a restaurant review dataset, from choosing sites to staying compliant.

Why Build a Restaurant Review Dataset?

No single site shows the full picture. For example, 71% of US consumers read reviews on Google, according to BrightLocal. However, people write reviews on many platforms: 45% on Google, 34% on Facebook, 24% on Yelp, and 16% on Tripadvisor.

On top of that, delivery apps collect their own ratings. So a restaurant can score 4.6 on Google but 4.1 on a delivery app.

A combined restaurant review dataset fixes that gap. Typically, these teams use one:

• Multi-location brands: compare outlets and spot weak stores early.
• Reputation management tools: show clients every review in one place.
• Market researchers and investors: benchmark brands and cuisines by city.
• Menu and operations teams: find recurring complaints, such as cold food or slow service.
• Expansion teams: check how rivals rate in a new area before opening.

Which Sites to Include in a Restaurant Review Dataset

Your restaurant review dataset should start with Google, then add the sites and apps that matter in your market. Here’s what each offers:

In India, add Zomato and Swiggy ratings, too. Similarly, add OpenTable or Deliveroo where they’re popular. For more on the delivery apps, see our review API page.

Three Ways to Fill a Restaurant Review Dataset

There are three main ways to collect reviews. Each suits a different need:

1. Official APIs for your own locations: Google’s Business Profile API can list all reviews for locations in your account. Similarly, DoorDash’s opt-in Reporting API includes reviews for your own stores. However, neither covers rivals.
2. Public listing APIs: Google’s Places API returns at most five reviews per place. Likewise, Tripadvisor’s Content API returns up to five reviews per location, with 5,000 free calls a month. So they work for samples, but not for full history.
3. Managed public data feed: a provider collects public ratings and reviews from every site, for you and your rivals. As a result, you get one feed, although it costs more than free tiers.

For most teams, mixing methods gives the best restaurant review dataset. For instance, use official APIs for your own outlets, then add a managed feed for rivals.

Build One Schema for Your Restaurant Review Dataset

Every site labels its data differently. So map each one into a single restaurant review dataset schema, such as this:

Normalise Ratings

Most sites use 1–5 stars. However, Just Eat UK rates delivery on 1, 3 or 5 points. Meanwhile, DoorDash asks diners to pick Loved, Liked, or Didn’t Like.

So convert every rating to a 0–100 score. First, subtract the scale’s minimum, then divide by its range and multiply by 100. For example, 4.2 on a 1–5 scale becomes 80.

Then weight each site’s average by its rating count. That way, a site with 12 ratings doesn’t outweigh one with 1,200.

Match Restaurants and Remove Duplicates

Each site names the same outlet slightly differently. For example, “Joe’s Pizza – Main St” on one site may be “Joe’s Pizza” on another. So match outlets in five steps:

1. Standardise names: use lower case, remove punctuation, and drop words like “restaurant”.
2. Compare addresses: standardise street names and postcodes first.
3. Check coordinates: treat outlets within about 100 metres as likely matches.
4. Use business phone numbers: they’re strong evidence where sites show them.
5. Review weak matches by hand: then save one master outlet ID for each restaurant.

Next, remove duplicates. Repeat captures often pick up the same review again. So deduplicate by site and review ID, or by a hash of site, outlet, date, and text. Also, flag closed or moved outlets, because old reviews can skew your restaurant review dataset.

Analyse Your Restaurant Review Dataset

Once your restaurant review dataset is clean, track a few clear KPIs:

• Blended rating: the count-weighted 0–100 score across all sites.
• Rating by site: where each outlet scores higher or lower.
• Review volume: new reviews per week, by outlet and site.
• Recency: the share of reviews from the last 90 days.
• Themes: common topics, such as food quality, delivery, service, and price.
• Reply rate: the share of reviews with an owner reply, where public.

For more on using reviews for research, see our guide to restaurant review aggregators.

Compliance and Personal Data

Reviews often include reviewer names, photos, and other personal details. So drop anything that identifies a person when you collect the data, because laws such as GDPR protect it. In addition, follow each site’s terms and attribution rules. Tripadvisor, for instance, requires you to credit it when you use its API. This isn’t legal advice, so check with a lawyer.

How FoodSpark Delivers Review Data

FoodSpark collects public ratings and reviews from review sites and delivery apps, outlet by outlet. Here’s what you get:

• Fields: rating, rating count, review text and date, as well as owner replies, where shown publicly.
• No personal data: we leave out reviewer names and profiles.
• One schema: every site mapped into the same fields, ready for your restaurant review dataset.
• Refresh: real-time, daily, weekly, monthly, or a custom schedule.
• Delivery: JSON, CSV, Excel, or XML, through our API or pushed to your database, cloud storage, or endpoint.
• Free sample: 100 rows in CSV or JSON, so you can test the data first.

See our reviews and ratings data service and our food data API.

We collect public data only, with no personal user data, payment information, or non-public content.

Get Started

Get Every Review in One Dataset

Want ratings and reviews from every site, matched by outlet and ready to analyse? Tell us your brands, rivals and cities. Then we will send a free 100-row sample of your restaurant review dataset.

Get a Free Review Dataset Sample

Restaurant Review Dataset: FAQs

What is a restaurant review dataset?

It’s a single table of ratings and reviews from several sites, mapped into one schema. So you can compare outlets and rivals on one scale.

Which sites should a restaurant review dataset include?

Start with Google, then add Yelp, Tripadvisor, and the delivery apps your diners use. In India, for example, add Zomato and Swiggy.

Can I get all Google reviews through an API?

Only for locations in your own Business Profile account. For other restaurants, the Places API returns at most five reviews per place.

How do I combine ratings from different sites?

In a restaurant review dataset, convert each rating to a 0–100 score. Then weight each site’s average by its rating count.

How do I match the same restaurant across sites?

Compare standardised names, addresses, coordinates, and business phone numbers. Then save one master outlet ID for each restaurant.

Is it legal to collect restaurant reviews?

It depends on each site’s terms and local laws, such as GDPR. So collect public data only, leave out reviewer names, and check with a lawyer.

How much does review data cost?

Overall, it depends on sites, outlets, fields, and refresh. So start with a free 100-row sample, and then get a custom quote.