Analysis
Sentiment Analysis for Tel Aviv D2C Cosmetics Brand
Overview
What this challenge is about.
You are provided with a dataset of 10,000 customer reviews (in English) with no labels. Your task is to preprocess the text, develop a sentiment classification model using NLP techniques (e.g., bag-of-words, TF-IDF, or word embeddings), and evaluate its performance. You must also produce a report summarizing key findings, such as common complaints or praised features. Success means achieving at least 80% accuracy on a held-out test set and delivering a clear business recommendation.
The Brief
What you'll do, and what you'll demonstrate.
How can GlowUp Naturals automatically extract sentiment from customer reviews to identify strengths and weaknesses of their new lipstick line?
Earning criteria — what you'll demonstrate
- Apply text preprocessing techniques (tokenization, stopword removal, stemming/lemmatization)
- Implement and compare different feature extraction methods (BoW, TF-IDF)
- Train and evaluate a supervised classification model (e.g., Logistic Regression, Naive Bayes)
- Interpret model results to derive actionable business insights
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career mappings coming soon.