Booking & TripAdvisorBooking & TripAdvisorExporting hotel reviews is the easy part. The hard part is what happens next: you now have five hundred reviews in a spreadsheet and no obvious way to turn them into a decision. Most people either skim a few and call it done, or reach for sentiment-analysis software that tells them what they already knew. Here is a method that is neither, and that works with nothing more than the spreadsheet you already have.
The instinct is to read everything. Resist it. For finding things to fix, positive reviews are close to worthless, because “lovely stay, would come back” contains no action. The signal is entirely in the criticism.
This is where the Booking.com structure earns its keep. Booking splits each review into a “liked” and a “disliked” part, so you can read the disliked column on its own and go straight to the complaints, skipping the praise entirely. On TripAdvisor, which blends praise and criticism into one body, the equivalent move is to filter to the one, two and three-bubble reviews and read those. Either way, you have just cut your reading by more than half without losing anything that matters.
The step that turns text into insight is tagging, and for a single property it is worth doing by hand.
Read through the negative reviews and give each a short theme label: cleanliness, check-in, noise, staff, value, food, maintenance. Keep the list short, five to eight themes, and reuse labels ruthlessly rather than inventing a new one for every review. It is dull, it takes an afternoon for a few hundred reviews, and it is the single highest-value thing you will do with the export.
Why by hand, when software exists? Because at one property, a person reading the reviews understands them better than any classifier, and the volume is small enough that automation saves no real time. Sentiment software earns its place when you are processing hundreds of properties, not when you are trying to understand one. There, the tagging becomes the bottleneck and automation is worth its inaccuracy. For your own hotel, your own eyes win.
Once every negative review carries a theme tag, the spreadsheet does the rest. Count the tags and you have a ranked list of what guests actually complain about. The top theme is almost never a surprise to the staff, but seeing it quantified, “forty percent of our negative reviews mention check-in”, changes the conversation from opinion to fact.
Two cuts make it sharper:
For a group, the same tagged data unlocks the comparison that a per-property star average hides completely. Line the properties up against the same theme tags and the outlier reveals itself: the one branch where “staff attitude” is the top complaint while everywhere else it is “parking”. That is a management signal you cannot get from Google’s or Booking’s blended scores, because averaging inside each property is exactly what buries it.
The step that separates review analysis from review theatre is going back. Fix the top theme, wait a month, re-export, and check whether the complaint fades from the new reviews. If it does, you have proof your change worked. If it does not, you learn that early instead of assuming success. Most people never do this second export, which is why most review analysis is a one-off report nobody revisits.
A sensible cadence is monthly: frequent enough to see a theme move, gentle enough on the platform to be responsible about request volume.
For this method to work, the export has to preserve the structure the analysis depends on: Booking’s liked/disliked split as separate columns, sortable dates rather than relative phrases, and the segment fields (trip type, country) when the platform shows them. Flatten any of those and you have made the analysis harder for no gain.
ExportReviews’s Booking and TripAdvisor exporter keeps the positive/negative split, normalises the ratings, and preserves the segment fields, so the spreadsheet you get is ready for exactly this workflow. If you have not exported yet, the step-by-step guide is the place to start.
Ignore the praise, tag the complaints by hand, count and trend the tags, compare across properties, then fix and re-export to check. It is unglamorous and it works, and it needs no software beyond the spreadsheet the export already gave you.
No, and for a single property it is usually a waste of effort. Sentiment scoring tells you a review is negative, which the star rating already told you. The value is in why it is negative, and the fastest route to that is a person reading the low-rated reviews and tagging themes by hand. Software helps at hundreds of properties, not at one.
By tagging. Read through the negative reviews and assign each a short theme label such as 'cleanliness', 'check-in', 'noise' or 'value'. Once every review carries a tag, you can count and trend them like any other column. The tagging is the work; the counting is trivial afterwards.
It is a shortcut straight to the complaints. Because Booking splits reviews into liked and disliked, you can read the disliked column on its own and skip the praise entirely, which is exactly what you want when you are hunting for things to fix. TripAdvisor blends the two, so there you read the low-rated reviews instead.
Enough that a theme repeats. A complaint that appears once is an anecdote; the same complaint across a dozen reviews is a pattern worth acting on. For most properties a few hundred reviews is plenty to separate recurring operational issues from one-off bad days.
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Export hotel and travel reviews from Booking.com and TripAdvisor to CSV, Excel or JSON. Both rating scales preserved, one extension for both platforms.
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