NLP Extractor Pipeline
1
Raw Corpus
Yelp JSON
2
TF-IDF Array
Vectorization
3
VADER / FLAIR
Polarity Signals
4
Logit Model
Binary Classifier
Yelp Review Rating / Sentiment Modeling
TF-IDF + logistic regression
What this proves
- Problem: needed to turn large volumes of review text into structured signals for customer sentiment and service issues.
- Method: applied text preprocessing, TF-IDF vectorization, VADER and FLAIR sentiment labeling, and logistic regression classification.
- Outcome: created a usable sentiment workflow that turns review language into repeatable service-improvement signals.
Live proof surface
This project currently has a recorded walkthrough plus downloadable project materials. It is a strong NLP and classification proof surface, but the public story stays focused on business-readable output rather than overstated model hype.