Online travel reviews contain detailed signals about visitor experience, yet converting this unstructured text into evidence that destination managers and travelers can use is non-trivial. This paper introduces a human-in-the-loop workflow that pairs qualitative coding with lightweight Large Language Model (LLM) analytics. Using Osaka, Japan, as a case study, we compile and pre-process reviews from Google Maps and Tripadvisor, distill them into a 12-topic schema that anchors automated topic and sentiment assignment, and validate model outputs against a human-coded subset using standard metrics. Integrated with exploratory analysis, the results yield a topic–sentiment matrix that highlights strengths and pain points across attractions and services. Findings indicate that a small LLM can deliver scalable, reliable polarity for clearly valenced texts, while the qualitative step sharpens topic granularity and guards against model drift. The pipeline is transparent and reproducible, and it generalizes to other destinations and platforms, enabling evidence-based destination management and more informed trip planning for travelers.