Hotel Reviews Sentiment Analysis & NLP Analytics
NLP data pipeline evaluating hotel guest reviews to classify customer sentiment, uncover operational friction points, and drive service quality improvements.
Technologies & Infrastructure Used
📐 System Architecture & Data Pipeline
Technical Diagram
Click on the diagram to expand in full resolution.
### 🎯 Business Problem & Analytics Objective
In the hospitality and tourism industry, customer feedback is largely unstructured text embedded within online booking reviews. Relying solely on numerical ratings masks critical operational details. The objective of this project was to design an NLP-driven analytics pipeline that converts unstructured guest feedback into actionable business intelligence for hotel operations management.
---
### ⚙️ Pipeline & Analytical Methodology
* **Text Preprocessing & NLP Pipeline:** Cleaned, tokenized, and normalized raw review text using NLTK and TextBlob, eliminating noise and standardizing vocabulary across guest feedback.
* **Sentiment Classification & Scoring:** Calculated polarity and subjectivity scores to categorize guest experiences into positive, neutral, and negative sentiment tiers.
* **Feature Extraction & Topic Identification:** Extracted recurring key phrases to correlate negative sentiment clusters with specific operational areas (e.g., room cleanliness, check-in delays, noise levels).
* **Data Visualization & Insights:** Synthesized findings into executive-ready visual breakdowns linking sentiment scores directly to overall rating metrics.
---
### 📈 Key Business Outcomes
* **Operational Friction Discovery:** Successfully highlighted primary drivers of negative reviews, enabling targeted operational interventions.
* **Automated Feedback Processing:** Scaled review analysis from manual sampling to an automated, reproducible data workflow.
📸 Project Gallery & Walkthrough Screenshots
Additional views, query executions, and dashboard components.
