You have 600 reviews across Google, Booking.com and Tripadvisor, in Italian, German, English and French. You read them as they arrive and reply to the negative ones, but you don't have an overall picture. Is night-time noise a rare or recurring problem? Has breakfast improved since last year's changes? What do German guests appreciate most?
Reading and classifying hundreds of reviews by hand takes days. With AI, a first analysis takes minutes. Let's look at how to do it usefully and reliably.
What AI can do with reviews
- classify each review by theme (cleanliness, staff, breakfast, noise, location, price, rooms, parking);
- assess tone (positive, neutral, negative) for each theme;
- find recurring problems and their frequency;
- compare periods (before and after a change);
- compare markets and languages;
- extract the words guests use for your strengths;
- summarise in a few lines what guests are saying.
The step-by-step method
1. Collect the reviews
Export or copy the last 12 months of reviews from Google, Booking.com and Tripadvisor (respecting platform terms). Remove unnecessary personal data (guests' full names).
2. Give AI clear instructions
Example instruction:
I'm giving you reviews of our hotel. For each one, state: language, themes mentioned (choose from: cleanliness, staff, breakfast, rooms, noise, location, parking, price, Wi-Fi, other), tone for each theme (positive/negative/neutral). Then summarise: the 5 most-mentioned strengths with 2 short quotes each, the 5 most-mentioned problems with frequency and 2 quotes each. Don't invent themes not present in the reviews.
3. Check the results
AI can make mistakes: spot-check some reviews and verify the classification is right. Check the reported problems in particular: they must correspond to real reviews.
4. Turn analysis into action
| Recurring problem | Frequency | Action | Owner | By |
|---|---|---|---|---|
| Noise in street-facing rooms | 18% of negatives | New windows + garden-side rooms on request | Owner | Winter |
| Slow Wi-Fi on upper floors | 12% | New repeater | Technician | 2 weeks |
| Breakfast: little savoury choice | 9% | Add 3 savoury items | Kitchen | Now |
5. Repeat and compare
Repeat the analysis every 3–6 months to see whether your changes worked.
Using strengths in marketing
The analysis also shows what guests really like, in their own words. Use them on your website, OTAs and ads. See how to use reviews to understand why guests choose your hotel.
Analyse competitors too
With the same method you can analyse direct competitors' public reviews: what their guests appreciate, what they criticise, where you can stand out.
Watch out for
- privacy: don't enter unnecessary personal data into AI tools;
- small samples: with few reviews, percentages are unreliable;
- interpretation: AI can misread irony or local expressions, especially across languages;
- decisions: the analysis suggests; you set the priorities.
Frequently asked questions
Do I need a dedicated review analysis tool?
To start, a good general AI assistant is enough. There are also online reputation tools that run these analyses automatically, useful at high volumes.
How many reviews can I analyse at once?
It depends on the tool. With many reviews, split them into batches (e.g. by quarter or platform) and then combine the results.
Can AI also reply to reviews?
It can draft replies, but every reply should be personalised and checked. See negative hotel reviews: how to reply without making things worse.
Want a full analysis of your hotel's reviews?
With our free marketing audit we analyse reviews for your hotel and competitors and give you a clear picture of strengths, recurring problems and priorities.
