Hotel Review Sentiment Analysis — Veetal Connect API

Hotel review sentiment analysis is the practice of turning free-text guest reviews into structured, measurable signals — sentiment polarity, topic-level scores and trends over time — so a hotel can see what guests praise or complain about instead of watching a single average rating drift. Veetal Connect API supplies the raw material for it: normalized guest reviews from Booking.com, Expedia, Google Hotels, TripAdvisor and Hotels.com through its Reputation datasets (reviews, rating and semantic analysis), already split by sentiment, traveler type and review category.

The interest is not academic. A systematic literature review published in ACM Computing Surveys catalogued the field precisely because hotel reviews had become one of the most studied text corpora in applied sentiment analysis (Ameur, Hamdi & Ben Yahia, 2023). And the operational payoff is measurable: in a Tripadvisor study run with Ipsos MORI across more than 23,000 users in 12 markets, 77% of travelers said they were more likely to book when owners respond to reviews (Tripadvisor, 2019). Knowing which reviews to answer, and about what, is a data problem.

Why the average score hides the problem

A property score is a lagging, lossy summary. By the time an 8.6 becomes an 8.3, hundreds of guests have already told you why — in text you never parsed.

• It averages away the actionable part. "Cleanliness" collapsing while "location" holds steady produces a barely-moving overall number and a very specific housekeeping problem.
• It ignores who is complaining. Business travelers and families rarely break on the same thing. A single score cannot separate them.
• It moves too slowly to act on. On a property with thousands of reviews, each new review shifts the mean by a rounding error, so the score reacts months after the operation does.
• It isn't comparable across channels. Booking.com's 0–10 scale, TripAdvisor's bubbles and Google's rating measure different populations with different prompts.
• It says nothing about your compset. A drop from 8.6 to 8.3 means one thing if your competitors dropped too, and something else entirely if they didn't.

Sentiment analysis exists to reverse that loss: go back to the text, tag it by topic and polarity, and rebuild a picture with enough resolution to assign work to someone.

What "at scale" actually requires

Analyzing fifty reviews is a spreadsheet exercise. Analyzing every review, on every channel, for every property in a portfolio, continuously, is an engineering problem with four hard requirements:

1. Complete retrieval, including history. Reviews are paginated and lazy-loaded on OTA sites. Missing pages silently biases every downstream metric, and you rarely find out.
2. One schema across OTAs. Scores on different scales, different category names and different traveler-type taxonomies must land in a single normalized model before anything is comparable.
3. Aspect-level granularity. Document-level polarity ("this review is negative") is nearly useless operationally. You need topic-level signal: staff, cleanliness, breakfast, noise, check-in, value.
4. A stable baseline over time and across competitors. A sentiment number without a trend line and a compset comparison is trivia, not a decision.

Scrapers struggle on all four. Site markup changes break selectors, anti-bot defenses interrupt collection, and what comes back is raw HTML that still needs parsing, deduplication and language detection before a model ever sees it — a pipeline you then own and maintain forever. An API removes that layer entirely and hands you the normalized records.

The signals reviews already carry, before any model runs

The most common mistake in review analytics is throwing an LLM at raw text while discarding structured fields the OTA already published. A review record from the reviews dataset typically arrives with:

• A native sentiment split. Booking.com stores each review in two halves, so the text arrives as positive_text (what the guest liked) and negative_text (what they didn't), either of which can be null. That is pre-labeled training and evaluation data you do not have to infer.
• Category scores. Google and TripAdvisor reviews carry per-aspect scores — rooms, service, location — out of 10, when the guest rated them. Numeric aspect ground truth, free.
• Traveler type. On Booking.com: solo, couple, family or friends, so sentiment can be segmented by the segment that actually matters to your positioning.
• Management responses. Whether the hotel replied, and what it said — the input for response-rate and response-quality analysis, and the lever behind the 77% figure above.
• Original language. Text arrives in the language it was written in, so you can route to the right model or analyze markets separately instead of flattening everything through translation.
• Source tagging. A single call to the Google reviews endpoint returns the reviews Google aggregates from every origin at once, each tagged with its source, so you can keep them separate or pass source=google to isolate Google's own.
• Masked identity. Reviewer names are obfuscated to an initial (R********), so the dataset is analysis-ready without carrying guest identities through your pipeline.

Use these first. Reserve the language model for what only it can do: clustering free text into topics and grading intensity — which is exactly what the semantic analysis dataset is for, extracting meanings and patterns from review text rather than re-deriving a score you already have.

Feed vs Real Time: pick the layer that matches the question

Veetal Connect API exposes reputation through two layers, and sentiment work usually needs both.

The distinction matters for sentiment specifically. Training or calibrating a topic classifier needs volume and history — that is the Feed layer, where reviews accumulate import after import and from_date/to_date slice the corpus. Answering "what are guests saying about this hotel right now" needs a live read — that is the Real Time layer.

A workflow that survives contact with production

1. Resolve identity once. Get the slug for each property from account_accommodations, and attach the competitor set you want to benchmark against. Everything downstream keys off that.
2. Backfill the corpus. Read the accumulated review history per OTA with feed_accommodation_reviews_by_ota, setting include_competitors=true so your compset lands in the same pull. Page through with limit/page and bound it with from_date.
3. Split the labeled signal from the unlabeled. Route positive_text/negative_text and category scores straight into your metrics. Send only the genuinely unstructured text to the model.
4. Classify into a fixed topic taxonomy. Staff, cleanliness, room, breakfast, noise, check-in, value, Wi-Fi. A fixed taxonomy is what makes week-over-week and hotel-vs-hotel comparison possible; a free-form one is not comparable to itself. The review sentiment analysis recipe does this against your own data in a single prompt, so you can see the taxonomy working before you commit to building one.
5. Anchor to the score, then alert on divergence. Track feed_accommodation_reputation_by_ota — overall score, review count and category scores for you and every competitor — and trigger when a topic's sentiment falls while the headline score hasn't moved yet. That gap is the early warning the average score can't give you.

Benchmarking sentiment against your compset

This is where reputation analysis stops being a report and starts being a decision. Because the Feed layer returns your property and its configured competitors in the same call and the same schema, you can compute topic-level sentiment deltas rather than absolute scores.

The useful question is never "is our cleanliness sentiment good?" — it's "is our cleanliness sentiment below the four hotels a guest sees next to us on the results page?" A property sitting at 8.4 in a compset averaging 8.1 has a pricing opportunity; the same 8.4 in a compset averaging 8.7 has a product problem. For a live, single-call read of Booking.com reputation across the whole compset, realtime_booking_reputation_compset returns your hotel and every competitor at once and bills per hotel actually consulted, not per request. The reputation pulse recipe assembles that comparison: your score, review volume and category breakdown against the comp set, on one scale.

Example request

Identity first, then data. For a live pull of a property's full Expedia review list:

GET /v2/real-time/{reference_type}/{identifier}/reviews
VEETAL-API-KEY: <api_key>

Reviews come back paginated and auto-paged, newest first, with scores, traveler type, stay context, text and management responses. Booking.com reviews have their own Real Time call, realtime_booking_reviews, which returns 100 reviews by default and up to 1,000 per call. On the stored side, GET /v2/feed/accommodation/{accommodation_slug}/booking-rating returns the Booking.com rating for a configured property — cheap enough to poll daily for a trend chart. Reference codes for cities, countries and currencies resolve for free through catalog_search.

If you are starting from the extraction side rather than the analysis side, our guide to extracting Booking.com reviews via API covers the retrieval layer in detail.

Data-quality notes worth designing for

Honest constraints, so your pipeline handles them deliberately instead of discovering them in a dashboard:

• Google review dates are derived. Google publishes a relative age ("10 months ago"), not a written-on date, so the date is inferred. The month is reliable; the exact day is not. Bucket Google-sourced sentiment by month, never by day.
• Review lists are capped by default. Endpoints return 100 reviews unless you raise limit, because paging a property with thousands of reviews takes tens of seconds. Set the limit explicitly for backfills and keep it low for polling.
• from_date needs the right sort order. On the Google endpoint it only applies with sort=newest, the one order actually sorted by date.
• Nulls are meaningful. In a sample of 1,200 recent Booking.com reviews, 43% had no text at all, only a score. Among the reviews with text, one in three left negative_text empty or wrote a filler such as "Nothing", and those guests averaged 9.3 against 7.4 for guests who wrote a complaint. Counting empty sides as neutral, or fillers as complaints, will bias every aspect model you build. The Booking.com reviews guide has the full breakdown.
• Language is a variable, not noise. Mixed-language corpora skew topic models. Segment by language or normalize deliberately.

Who runs this in production

• Hotels and chains — convert thousands of comments into a ranked list of operational priorities, and see whether a dip is theirs or the market's.
• Reputation and marketing agencies — monitor dozens of properties across OTAs on one scale and produce client reporting without a scraper to babysit.
• Revenue teams — correlate topic-level sentiment with rate movements and the compset, using reputation as a pricing input rather than a vanity metric.
• BI and data teams — feed normalized guest sentiment into the same warehouse as rates, availability and demand, from the same reputation data type.

A concrete pattern: an agency managing 40 hotels reads the Feed layer each morning for Booking.com, Google and TripAdvisor, sends only new unstructured text through semantic analysis, and alerts a property when a topic like cleanliness drops two weeks running relative to its compset — while the headline score is still unchanged.

When the output is for guests rather than an internal dashboard, the reputation panel for your own website recipe renders the same signal on your own site, behind a proxy that keeps the API key server-side.

Frequently asked questions

What is hotel review sentiment analysis? It is the process of converting free-text guest reviews into structured signals — sentiment polarity and topic-level scores such as cleanliness, staff or breakfast — so hotels can measure what guests actually say instead of tracking a single average rating. It is normally run continuously across every OTA a property appears on.

Can I analyze hotel reviews at scale through an API? Yes. Veetal Connect API returns normalized guest reviews from Booking.com, Expedia, Google Hotels, TripAdvisor and Hotels.com as structured JSON, including review text, category scores, traveler type and management responses, so the analysis layer runs on clean records instead of scraped HTML.

Do I need my own machine-learning model? Not to start. Booking.com reviews already arrive split into positive and negative text, and Google and TripAdvisor reviews carry per-category scores — that covers a large share of aspect-level reporting with no model at all. A model adds value for topic clustering and intensity grading on the remaining free text.

Which OTAs can I collect review sentiment from? Booking.com, Expedia, Google Hotels, TripAdvisor and Hotels.com, normalized to a single schema so scores and categories are directly comparable across channels.

How do I compare my sentiment against competitors? Configure a competitor set on each property, then read reviews and reputation with competitors included. The Feed layer returns your hotel and its compset in the same call and schema, so you can compute topic-level deltas rather than absolute scores.

How far back does review history go? Reviews accumulate import after import in the Feed layer, so it holds the full history collected for your configured properties; narrow it with from_date/to_date, or read a specific import_id to reconstruct how a score evolved.

How much does it cost to test this? You can start with 1,000 free credits, no credit card required. Feed reads are cheap, Real Time calls consume credits, and caching is configurable from 0 to 24 hours to keep repeated lookups from spending twice.

Turn your reviews into a metric you can act on

Stop watching the average and start reading the text. Get normalized guest reviews, category scores and management responses from every major OTA through one API — and put a real number on what your guests are telling you. Start free with 1,000 credits (no credit card), or review pricing.