Online reviews have become one of the most commercially valuable data sources in hospitality. A hotel's TripAdvisor score, its aggregated Booking.com ratings, its Google review profile — these directly influence booking decisions for a significant share of travelers. Yet most hotels still manage their review response process manually and analyze their review content intuitively rather than systematically. Natural language processing (NLP) is the technology that changes this equation, and this paper provides one of the most thorough surveys of how it works and where it's being applied.
The paper develops a taxonomy of NLP techniques used to analyze customer reviews — organizing a complex field into a structure that non-technical readers can actually use to evaluate tools and vendors. The core methods break into three generations. First-generation lexicon-based approaches work by matching words in reviews against pre-built lists of positive and negative terms (think: a dictionary of "great," "excellent," "clean" as positive versus "dirty," "rude," "broken" as negative). These tools are fast, inexpensive to run, and reasonably accurate for simple sentiment scoring — they're what most basic review management platforms use today.
Second-generation machine learning methods train models on large datasets of labeled reviews to recognize patterns that simple dictionaries miss — nuance, context, category-level sentiment (a guest can love the rooms and hate the breakfast in a single review). These models outperform lexicon approaches significantly on accuracy and are now the backbone of more sophisticated hotel analytics platforms.
Third-generation transformer models — including systems related to ChatGPT — represent the current state of the art. These models can understand complex linguistic structures, recognize sarcasm (a persistent failure mode for simpler systems), handle multilingual reviews without translation errors, and extract fine-grained insights about specific service elements or staff behaviors. Their accuracy on hospitality review datasets is substantially higher than earlier methods, though they're more computationally expensive to run.
The practical value for hotel operators is clearest in three workflows. Reputation monitoring: instead of reading every review manually, NLP tools can flag emerging issues (a new complaint about WiFi in room 301, a pattern of comments about check-in wait times on Friday afternoons) at a speed and scale no human team can match. Competitive benchmarking: the same NLP tools can analyze competitors' reviews to identify service gaps and opportunities. And response generation: AI-assisted drafting of review responses saves time and improves consistency — though human review before posting remains strongly recommended.
The paper also highlights open challenges that buyers should ask vendors about directly: performance on non-English reviews (accuracy drops significantly in many tools for languages other than English and Spanish), handling of ironic or culturally specific expressions, and the rapid obsolescence of training data as language patterns evolve.
For revenue and marketing leaders, the bottom line is straightforward: your reviews are an underutilized intelligence asset. NLP tools — which are now commercially available and affordable at the level of individual properties, not just chains — can turn that unstructured text into actionable insight on a weekly or even daily basis. The gap between hotels doing this systematically and hotels not doing it is becoming a meaningful competitive disadvantage.