HoReCa Feedback Classifier

HoReCa Feedback Classifier is a pre-trained AI model specifically designed for the hospitality, restaurant, and catering industry. It automatically analyzes consumer feedback and classifies it into 13 key categories: Food & Beverage Quality, Menu Variety & Options, Service & Staff, Cleanliness & Hygiene, Atmosphere & Ambience, Pricing & Value, Booking & Reservations, Accommodation Quality, Facilities & Amenities, Location & Accessibility, Delivery & Takeaway, Events & Entertainment, and Brand Perception & Trust.

Trained on large datasets of HoReCa-related feedback, the model processes unstructured text such as guest surveys, restaurant reviews, hotel booking platforms, or social media posts. Each entry is automatically mapped to the most relevant category. If feedBeyond Keywords: Understanding HoReCa Feedback

Keyword-based methods often fail to capture the nuance of hospitality feedback. For example:

• “The food arrived cold” could be misclassified under Delivery when it directly relates to Food & Beverage Quality.
• “Staff were friendly but service was slow” might get tagged as Atmosphere instead of Service & Staff.
• “The lobby looked elegant” could be mistakenly assigned to Cleanliness instead of Atmosphere & Ambience.

HoReCa Feedback Classifier applies context-aware semantic analysis, recognizing industry-specific expressions, multi-topic feedback, and cultural nuances—ensuring accurate classification across the 13 hospitality categories.back does not clearly fit any label, the model outputs “None”, ensuring ambiguous or irrelevant comments are filtered rather than misclassified. By structuring raw feedback, hospitality businesses can identify consumer expectations, spot service gaps, and improve guest satisfaction at scale.

Spanning over 30 languages such as English, Spanish, French, German, and Dutch, the model helps hospitality brands listen to their guests no matter where they are. This multilingual reach ensures feedback is interpreted consistently across regions, giving managers a clear, unified view of their global operations.

Unlocking Value from Hospitality Feedback

From guest surveys to online reviews, hospitality brands receive an endless stream of feedback. Manually making sense of it all is both slow and prone to mistakes. With this model, the process is automated—turning raw opinions into structured insights within seconds.

• Restaurants to evaluate food quality, menu variety, and delivery experiences,
• Hotels to assess accommodation, facilities, and location-related feedback,
• Catering companies to monitor events and service quality,
• Hospitality managers to measure staff performance, pricing fairness, and brand perception.

Example Scenario: A hotel chain launches a new resort. Within weeks, it receives thousands of reviews mentioning spacious rooms, friendly staff, high prices, and slow Wi-Fi. The model automatically classifies feedback into Accommodation Quality, Service & Staff, Pricing & Value, and Facilities & Amenities. Managers can then address weaknesses while promoting strengths in marketing campaigns.

Kimola’s Difference

Kimola’s HoReCa Feedback Classifier offers more than automated tagging:

• Industry-specific taxonomy covering 13 hospitality domains,
• Context-aware semantic analysis to avoid keyword misclassification,
• Scalable architecture for processing thousands of reviews daily,
• Multilingual support for international hospitality brands,
• Actionable insights that connect guest feedback directly to operations, marketing, and brand management.

By focusing on hospitality-specific feedback, the model transforms guest opinions into business intelligence, helping hotels, restaurants, and catering services enhance quality, optimize pricing, and build stronger brand trust.

Try It Yourself

Use the console above to test the model. Paste a guest review, restaurant comment, or booking platform feedback, and instantly see how it’s categorized into Food & Beverage, Service, Atmosphere, Pricing, Cleanliness, or other HoReCa domains. Testing with your own data shows how the model interprets real hospitality feedback and highlights opportunities for service improvements.