Hotel AI Radar: How AI and semantic analysis have changed the rules of the game in hotel reputation management

While in the first installment of EMEXS’s Hotel AI Radar we defined the key metrics of the new search ecosystem, in this edition we explore how generative engines read and evaluate the customer experience. The overall rating on traditional platforms is no longer the sole deciding factor: the semantic analysis performed by large language models (LLMs) now processes the original text of reviews to extract specific attributes and determine which properties most accurately match the traveler’s actual intent.

In this second edition, we break down the scientific evidence on natural language processing (NLP) in reviews and offer a practical guide for adapting online reputation to the logic of conversational search.

How semantic analysis of Large Language Models has changed the weight given to hotel reviews

Although a hotel’s online reputation has always been based (for many) on the average rating on its Google Business Profile or on platforms such as Booking and TripAdvisor, more and more industry professionals have stopped obsessing over these ratings following the emergence and growing influence of artificial intelligence in the sector.

Until now, the standard strategy was to achieve a rating of 8.5 or 9.0 on Booking and 4.5 on TripAdvisor or Google Business Profile, respond to reviews as quickly as possible (either manually or automatically using in-house or third-party systems to maintain a high response rate), and trust that the algorithm would keep the establishment in the top rankings.

However, with the rise of conversational search and generative response engines (such as Google AI Overviews, ChatGPT, and Gemini), the quantitative aspect is becoming less important.

Artificial Intelligence does not process isolated numbers; it analyzes natural language. A clear example of this occurs when we enter a prompt like this into Gemini:

• “Tell me about a quiet hotel near the beach on the Costa Brava that has good Wi-Fi and a good breakfast.”

Large Language Models process the unstructured text written by guests to verify whether their actual experiences match the specific attributes requested by the user.

Even if you don't specify that you're looking for a “4-star” hotel or one with “more than 8 points,” the system assumes you want a high-quality place. If two hotels exactly match your request for “quiet and a good breakfast,” the AI will prioritize the one with the best overall ratings to avoid recommending a subpar place.

The rating is no longer the initial screening criterion but rather a supporting factor. The model first identifies which hotels meet the contextual criteria mentioned in the reviews (“reliable Wi-Fi” and “quiet area”) and then uses the rating to rank the best options.

A hotel with an excellent rating (4.9/5), but whose reviews mention “loud music until midnight,” will be excluded from a search for “peace and quiet.” On the other hand, a hotel with a lower overall rating (4/5) that stands out precisely for its quiet atmosphere and workspace will be recommended.

How does AI “understand” the text of a review?

To understand the exact mechanics of how an LLM evaluates a comment, we must turn to the scientific evidence. A study published in August 2025 by researchers at the Universities of Twente and Marburg (A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models) demonstrates experimentally how AI models break down a customer's opinion into structured data.

The research analyzes the technique known as ABSA (Aspect-Based Sentiment Analysis).

What is ABSA analysis, and how does it work?

Unlike a traditional analysis that labels an entire review as “positive” or “negative,” the ABSA architecture breaks down each sentence into three components:

• Attribute or aspect: The specific element mentioned (Wi-Fi, noise, breakfast, staff service).
  Feeling: The polarity associated with that specific aspect (positive, negative, or neutral).
  Entity mapping: The relationship between the attribute and the overall customer experience.

Example of an ABSA analysis in a review:

• Review: “Very clean room and friendly staff, but the Wi-Fi wasn't working and it was very noisy at night.”

Processing an LLM using ABSA analysis would yield the following results:

• Cleanliness: Feeling: POSITIVE
• Atention: Sentiment: POSITIVE
• Connection: Feeling: NEGATIVE (Attribute: Wi-Fi)
• Rest: Mood: NEGATIVE (Attribute: Nighttime noise)

To test the accuracy of the process, the researchers created a manually annotated corpus containing more than 10,800 real Google Maps reviews and more than 16,900 labels for specific aspects. After evaluating the performance of models such as GPT-4 and LLaMA-3, the study reached three key conclusions:

• Accuracy greater than 85%: Current LLMs accurately identify key attributes and their sentiment in unstructured text.
• GPT-4 outperformed LLaMA-3: It demonstrated superior performance in fine-grained feature extraction and in detecting nuances across multiple languages.
• Inconsistency Detection: The AI identifies when the numerical rating does not match the text. If a guest gives a 9/10 but writes, “The noise kept me awake,” the LLM ignores the 9 and classifies the establishment as “not suitable for rest.

Operational implications for the hotel industry

These data confirm that user-generated content (UGC) influences the perception of value, where value is understood as what the customer feels they are receiving in exchange for their money and the willingness to pay, rather than the overall rating. The transition to semantic search requires adapting reputation management through three strategic approaches:

• Adjectives take the place of numbers: The visibility of AI assistants depends on the frequency and consistency of key concepts in real reviews. If dozens of guests mention “varied breakfast” or “impeccable soundproofing,” the AI will recommend the hotel for those searches with a high degree of confidence.
• Semantic audit of traditional metrics: Monitoring only the average rating or NPS is insufficient. Revenue Management and Marketing teams must perform sentiment analysis on raw text to identify which specific negative attributes are driving search engine response algorithms like Google’s.
• AI-Driven response strategy: Generic corporate templates lack semantic value. The response should serve as a data update that informs the model about the resolution of the detected problem.

Practical Guide: How to structure answers to optimize semantic ranking

For generative search engines to interpret a hotel’s response positively, it must follow a structured semantic framework based on three pillars

• Direct recognition of the aspect (Attribute): Explicitly mention the concept cited by the customer (Wi-Fi, soundproofing or pool temperature).
  Evidence of action or operations (Contextualization): Provide specific details about the solution implemented or maintenance performed. This allows the model to counteract the previous negative attribute.
  Positive semantic reinforcement (Entity keywords): Naturally incorporate key concepts the hotel wants to rank for (hotel for remote work, guaranteed rest, fresh breakfast).

Comparative examples: Traditional responses vs. AI-Optimized responses

Here are three examples of how to respond to a review so that AI interprets it positively.

Case 1: Negative review due to poor Wi-Fi connection

• Guest review (9/10): “The hotel is spotless, but the Wi-Fi in room 304 kept cutting out, and I had a really hard time working from there.”

Traditional response (not highly valued by LLMs)

• “Hi Juan, thank you for your review and your high rating. We're sorry about the Wi-Fi issues; we'll take note of this so we can continue to improve our service. We hope to see you soon!”

AI result: Records the entity “Hotel X” with the attribute “Unstable Wi-Fi / Not suitable for remote work.”

AI-Optimized response (semantic resolution

• "Hi Juan, thank you for sharing your experience in detail. Following your comment, our technical team has checked the access point on the 3rd floor and replaced the router in room 304 with a high-speed fiber-optic device (300 Mbps). At our hotel, we are committed to providing an optimal environment for remote work and uninterrupted connectivity. We look forward to welcoming you back so you can see the improvement for yourself."

AI result: Records “Wi-Fi corrected/300 Mbps” and reinforces the entities “remote work environment” and “high speed.”

Case 2: Negative review due to noise at nigh

• Guest review (8/10): “The location is perfect, but the street noise on Friday night kept us from getting a good night's sleep.”

Traditional response (not highly valued by LLMs

• “Dear Maria, we're very sorry that the noise affected your stay. That's sometimes the downside of a central location. Best regards.”

AI result: Confirms the negative attribute “Noisy room / Poor sleep.”

AI-Optimized response (semantic resolution

• “Dear Maria, thank you for highlighting our excellent central location. We apologize for the occasional inconvenience caused by traffic outside. We would like to inform you that we have completed the installation of soundproof double-paned windows in all rooms facing the main façade, thereby ensuring sound insulation and a restful night's sleep for our guests. We invite you to experience these improvements during your next visit.”

AI result: Update the attribute to “Double-glazed / Soundproof / Guaranteed rest.”

Case 3: Positive review (Semantic reinforcement

• Guest review (10/10): “I loved my stay; the staff was very friendly, and the orange juice at breakfast was delicious.”

Traditional response (not highly valued by LLMs

• “Thank you so much for your review! We're glad you liked everything.”

AI result: Does not provide any additional semantic density.

AI-Optimized response (attribute enhancement

• “Thank you so much, Carlos! We're glad to hear you enjoyed our breakfast buffet featuring local products and freshly squeezed orange juice. Our front desk team works every day to provide friendly and personalized customer service. We hope to see you again soon!”

AI result: Index the hotel for searches such as “local breakfast buffet” “freshly squeezed juice” and “personalized service.”

Today, with AI-powered searches, a hotel’s reputation is no longer measured by the static rating displayed on its listing, but rather by the structured insights that artificial intelligence derives from analyzing the body of real reviews written by its guests.