AI-Powered Guest-Review Sentiment Analysis Dashboard for a Multi-Location Hotel Brand

Overview

Built an AI-powered dashboard for a multi-location hotel brand that pulls guest reviews from every major platform and analyzes sentiment across common themes — cleanliness, staff friendliness, room comfort, value — instead of leadership reading through hundreds of individual reviews. The dashboard highlights which specific themes are trending negative at which property, giving regional managers an early signal of an operational issue before it shows up as a falling overall rating. This replaced a manual quarterly review process where issues were often identified months after guests first started noticing them. The model was trained to handle the brand's specific terminology and property types rather than using a generic sentiment classifier. Leadership at a multi-location hotel brand relied on a manual quarterly review process to catch guest-feedback issues, often identifying problems months after guests first started noticing them. We built an AI-powered dashboard pulling guest reviews from every major platform and analyzing sentiment across common themes — cleanliness, staff friendliness, room comfort, value — highlighting which themes are trending negative at which property before it shows up as a falling overall rating. We trained the sentiment model on a large sample of the brand's own historical reviews to handle its specific terminology and property types, then validated theme classifications against a manual quarterly review for accuracy before replacing that process entirely. The time to detect an emerging property-level issue dropped from a full quarter to within a couple of weeks, giving regional managers a real early-warning signal based on actual guest feedback trends instead of a lagging quarterly snapshot.