NLP Extractor Pipeline
Review Rating Classification
The Problem
Massive volumes of raw customer review text were completely invisible to standard KPI reporting, leaving operations teams blind to repeating service bottlenecks.
The Methodology
Built an NLP pipeline combining Python text-preprocessing, advanced TF-IDF vectorization, pre-trained VADER/FLAIR sentiment analyzers, and a tuned logistic regression classifier.
The Impact & Outcome
A robust sentiment engine that correctly isolates positive hospitality traits from recurring negative feedback clusters with high efficiency.
Key Metric: High-accuracy text classification transforming angry and positive paragraphs directly into isolated, actionable operational feedback signals.
Classification Pipeline Walkthrough
Below is the recorded presentation outlining the classification model accuracy, tokenization workflow, and text-mining pipeline application out in the wild.