This is like giving a computer a big pile of hotel reviews and asking it to automatically tell you which guests were happy, which were angry, and what they talked about most—without a human needing to read every review.
Manually reading and tagging thousands of hotel reviews to understand customer satisfaction is slow, expensive, and inconsistent. The system uses natural language processing to automatically classify review sentiment (positive/negative/neutral) and surface key themes, enabling hotels and consumer brands to track customer satisfaction at scale and respond faster to service issues.
Primarily methodological know‑how and labeled review datasets; defensibility comes from domain-specific tuning on hotel/customer-review data rather than unique algorithms.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Model performance depends heavily on quality and volume of labeled review data; retraining is needed as language and guest expectations evolve.
Early Majority
Focused specifically on hotel/customer reviews with NLP techniques, likely using domain-specific preprocessing and feature engineering (e.g., handling informal language, spelling errors, and hospitality-specific terms) rather than generic sentiment tools.