How to Analyze Restaurant Reviews With AI: A 5-Step Method

A general manager reads the new reviews every morning. They catch the emergencies, rarely what repeats from one location to the next. From a single store, nobody sees that takeout is dragging down three restaurants out of five. AI can: with ChatGPT or Claude, analyzing your restaurant reviews takes seconds, starting from a single file.

Add your DoorDash and Uber Eats feedback and you can finally compare the dining room with delivery. There is one condition nobody mentions: the file has to be clean. During our September 30, 2026 webinar, it took 25 minutes to merge two platforms, Google and Uber Eats, before asking a single question. That is where most attempts stall.

Here is the 5-step method, a ready-to-use prompt and the limit to know before rolling it out across a whole group.

Key takeaways

• What AI does well: compare hundreds of reviews by location and channel, and count the issues that keep coming back.
• The real cost: getting the exports. Google offers Takeout and an API, Yelp no bulk export, DoorDash a portal view.
• Platforms don't rate the same way: stars per location on Google and Yelp, a three-level rating per order on DoorDash, thumbs per dish on Uber Eats. Averaging them gives a meaningless number.
• The right question: a ratio of positive to negative mentions by theme and location. "Summarize my reviews" is the worst prompt you can write.
• The data: a business plan (ChatGPT Business or Enterprise, Claude Team or Enterprise) and no reviewer names in the file.
• The limit: an export is a snapshot. Beyond two or three platforms, merging by hand every month does not hold.

Summarise this article with:

C ChatGPT C Claude P Perplexity G Grok

Why use AI instead of reading reviews one by one?

One review tells you what one guest experienced. A hundred reviews analyzed together tell you what repeats, where and on which channel. The same issue can hit three locations out of five, or only delivery, without anyone noticing.

Reading reviews every morning is not a way to run a group. You see the fires, not the trends. AI groups hundreds of comments by theme (wait time, food temperature, service, packaging, missing items), counts them and splits them by location.

Sensitive reviews still get a human read. The rest becomes a priority list. In our guide to using ChatGPT and Claude in a restaurant group, this method is level 2: uploading a file.

Step 1: export your Google, Yelp and delivery reviews

Gather three to six months of reviews, location by location, on each platform. The goal is one file per platform, covering the same period for every location.

Google comes with a paradox. Business Profile lets you read and reply to every review, but not export them cleanly. Two doors remain. The free one is Google Takeout: select only "Google Business Profile" and run the export from the owner or manager account. The archive holds one reviews file per location, in JSON.

You then convert it into a table, with Power Query in Excel or by asking the AI to do it. Ratings arrive spelled out ("FIVE", "FOUR") and must be turned into numbers first. Fine for a one-off test, much less so every month across ten locations.

The technical door is the Google Business Profile API. It returns 50 reviews per call, works only on verified listings and requires a Google Cloud project with approved access. In a group, nobody wires it by hand: that is the job of a connected tool.

Yelp is the hardest. There is no bulk review export for business owners, and the Yelp API returns up to three review excerpts per business. For a first test, start with Google and delivery, and bring Yelp in through a connected tool later.

Delivery splits in two. On DoorDash, reviews live in the Merchant Portal under Customers, then Ratings & Reviews, filterable by date over up to 90 days. DoorDash's help center describes no download. On Uber Eats, the Reports section of Uber Eats Manager lets you download customer and menu item feedback.

Step 2: merge your reviews into one format

The four platforms don't speak the same language. Before handing anything to AI, bring everything into one table: one row per review, the same columns everywhere. The table below shows what changes from one platform to the next.

Keep at least seven columns: location, platform, date, sentiment (positive, neutral or negative), channel, review text, and replied yes or no.

Then delete reviewer names. Even when published, a name is still personal data, and it adds nothing to the analysis.

A word on ratings: never average a Google score with a DoorDash or Uber Eats score. They don't measure the same thing, and the result means nothing. This merging step is what took 25 minutes during our demo, for two platforms only.

Step 3: upload the file to ChatGPT or Claude

A clean CSV or Excel file is enough: both ChatGPT and Claude can read it and run the math. First check: ask how many reviews it read and across how many locations. If the count is wrong, stop there.

File size is rarely a problem. ChatGPT accepts spreadsheets of about 50 MB, according to OpenAI's file uploads FAQ, enough for several thousand reviews. Claude accepts 30 MB per file and up to 20 files per chat, according to its help center.

One rule is not negotiable: a business plan, never a personal account. It is what keeps company data out of model training and puts access under admin control. Our guide to what customer data restaurants can share with ChatGPT and Claude covers the privacy guardrails in detail.

Step 4: ask AI the right question

"Summarize my reviews" is the worst thing you can ask. You will get a lukewarm, accurate and useless summary. Ask for counts, ratios and comparisons, and require every answer to end in an action you can assign to a location.

You are a quality analyst for a restaurant group. The attached file holds our Google, DoorDash and Uber Eats reviews from the last 6 months, one row per review. 1) Group the comments into 8 themes at most. 2) For each theme, count positive and negative mentions by location and by channel. 3) Rank the 3 issues with the worst negative-to-positive ratio, with 2 quotes each. 4) Suggest one concrete action per issue. Do not invent any number: if data is missing, say so.

During the webinar, a question like this surfaced a priority no dashboard in the group had isolated. Takeout drew 2 positive mentions against 12 negative ones, the worst ratio in the whole dataset.

For more question ideas, see our ChatGPT prompts for restaurant strategy.

Step 5: check the analysis and turn it into decisions

AI can put a review in the wrong bucket or round generously. Before you present a number to leadership, ask for the quotes behind each theme and read about ten of them. Then recount one theme at one location yourself. If the numbers match, the analysis holds.

Next, name an owner and a deadline for each issue, and run the same question the following month to measure the effect. An analysis that does not end with a name and a date was a waste of time.

Did the analysis reveal unanswered reviews? Our 30 Google review response examples help you catch up, and an AI project in your brand voice keeps replies consistent across locations. Keep a human read on sensitive reviews, such as food poisoning or an allergic reaction.

How do you manage reviews for multiple restaurant locations?

For a few locations and two platforms, the manual method holds once a month. Beyond that, it breaks. Every platform you add makes the merge heavier, Yelp, OpenTable or Tripadvisor among them, and the file is outdated the day after the export.

AI analyzes what you give it very well. It only fetches reviews itself if you connect it to your tools: that is what Claude and ChatGPT connectors do, explained without jargon.

Past five or six locations, you need all your reviews in one tool, connected to every platform continuously. The analysis then always starts from fresh data, and replies stay consistent across locations, following the best practices in our restaurant review management guide.

Want to see the difference between a hand-merged export and connected sources? Watch the webinar replay with Waad Toumi and Adrien Angelvy, on real restaurant data.

Frequently asked questions

How do I export my Google reviews for free?

Use Google Takeout. Sign in with the account that manages your listings, select only "Google Business Profile" and start the export. The archive holds a reviews file per location, which you convert into a table before analysis. For monthly tracking across several locations, a tool connected to the Google Business Profile API saves you from repeating it by hand.

Can I export my Yelp reviews?

Not in bulk. Yelp lets business owners read and reply to reviews, but offers no bulk export, and its API returns up to three review excerpts per business. For a one-off analysis, start with Google and your delivery platforms. To include Yelp every month, use a review management tool connected to the platform.

How do I analyze my restaurant reviews with ChatGPT?

Export your reviews from each platform, merge them into one CSV or Excel file with one row per review and shared columns (location, platform, date, sentiment, channel, text), then upload it to ChatGPT on a business plan. Ask a precise question: group comments by theme, count positive and negative mentions by location, and rank issues by worst ratio.

How do I compare Google reviews with DoorDash and Uber Eats feedback?

Bring them to a common scale. Google rates the location from 1 to 5 stars, DoorDash rates each order as Loved, Liked or Didn't Like, and Uber Eats collects thumbs per dish. Tag each review positive, neutral or negative, add a channel column, then compare the themes that come up in the dining room and in delivery, location by location.

Is it safe to upload customer reviews to ChatGPT or Claude?

Yes, with two precautions. Use a business plan (ChatGPT Business or Enterprise, Claude Team or Enterprise), where your data does not train the models by default, and remove reviewer names before uploading: even published online, a name is personal data. The review text is all the analysis needs.

At Malou, we help restaurant groups manage their local visibility — listings, reviews, content — across every location, and bring reviews from every platform into one place to analyze them without manual exports. Test your group's visibility with our diagnostic tool.