Hotel Review Sentiment Analysis API

Semantic analysis turns the free text of guest reviews into something you can measure: sentiment and topics, extracted from what guests actually wrote rather than summarised into a single average that moves too slowly to act on.

Why the average score hides the problem

By the time an 8.6 becomes an 8.3, hundreds of guests have already told you why — in text nobody parsed. Cleanliness can collapse while location holds steady and the overall number barely moves. The average is a lagging, lossy summary; the text is where the actionable part lives.

What the analysis produces

Review text classified into topics — cleanliness, staff, breakfast, noise, check-in, value — each with a sentiment reading, so thousands of comments become a ranked list of operational priorities instead of one number. Because it runs on the reviews dataset, it works across every OTA at once and on your competitor set, not only on your own property.

A fixed taxonomy is what makes it comparable

Topics have to be stable to be useful. A fixed set lets you compare week over week and hotel against hotel; a free-form one is not even comparable with itself. The review sentiment analysis recipe runs this against your own data in a single prompt, so you can see the taxonomy working before committing to build one.

The signal worth alerting on

Not the level, the divergence: a topic whose sentiment is falling while the headline score has not moved yet. That gap is the early warning an average cannot give you, and it is the one that translates into a task for housekeeping or the front desk.

Who uses it

Operations teams turning comments into priorities, reputation agencies reporting across portfolios, and revenue teams using topic-level sentiment against the comp set as a pricing input rather than a vanity metric.