🏨 Cross-Cultural NLP Analysis of Luxury Hotel Reviews in Europe — LDA Topic Modeling

🌍 Overview

Conducted a comprehensive NLP analysis on 515,000 luxury hotel reviews across Europe to uncover latent customer satisfaction patterns and regional sentiment differences.
This project combines unsupervised topic modeling (LDA, NMF, Clustering) with cross-cultural analysis to explore how travelers from different nationalities prioritize various aspects of luxury stays.

Goal: Go beyond ratings — understand why guests feel satisfied or disappointed, and reveal how cultural factors influence luxury travel experiences.

🧩 Dataset & Preprocessing

Data Source: Kaggle — “515K Hotel Reviews Data in Europe” (Booking.com)
Size: 515K reviews from 1,493 hotels across Europe
Cleaning & Structuring:
- Removed duplicates, handled missing data, trimmed whitespace
- Created a concept hierarchy for regional groupings based on Google Maps
- Tokenized, lemmatized, and vectorized reviews using TF-IDF and Bag-of-Words

🧠 Methods

Applied a suite of unsupervised learning algorithms for thematic and cluster-based segmentation:

TF-IDF + K-Means Clustering: Identify high-level review groupings
LDA (Latent Dirichlet Allocation): Extract interpretable latent topics
NMF (Non-negative Matrix Factorization): Validate topic coherence and sentiment separation
Comparative Analysis: Combined with R and Tableau for quantitative and visual evaluation

🔍 LDA Topic Modeling (Gensim Implementation)

Corpus Construction:
- Built dictionary → Bag-of-Words vectorization → TF-IDF transformation
Optimization:
- Tuned over 50 iterations to maximize topic coherence
- Adjusted num_topics, passes, and iterations for model stability
Results:
- High coherence across models
- Meaningful topic clusters reflecting distinct traveler preferences

📊 Findings

Regional Insights:
- Europe: Focused on noise, comfort, and value for money
- Asia & Africa: Strong emphasis on meals and dining quality
- America: Value for money drives most negative reviews
Positive Review Themes: Cleanliness, service, and staff hospitality
Negative Review Drivers: Noise, food dissatisfaction, and operational inefficiency
Cultural Context: Eastern Asia valued customer service highest — unmet expectations amplified negative sentiment

🖼️ Presentation & Deliverables

📎 Slides: Hotel Review Analysis Slides (PDF)

📄 Full Report: Hotel Review Analysis Report (PDF)