Build a Multilingual Text-Mining Dashboard for Hotel Reviews
Overview
What this challenge is about.
You receive 200,000 sampled reviews across 9 languages plus an English-only labeled benchmark of 1,000 reviews for sentiment and aspect (rooms, food, staff, value, location). Build a pipeline that does (1) language detection, (2) multilingual sentiment (XLM-RoBERTa or a smaller distilled model), (3) aspect extraction with a fine-tuned tagger, (4) topic clustering with BERTopic. Deliver a small Streamlit dashboard plus a 3-page evaluation report against the English benchmark and a side-by-side cost comparison vs. the vendor API.
The Brief
What you'll do, and what you'll demonstrate.
Replace a vendor sentiment API with an open-source multilingual text-mining stack that surfaces aspect-level signals at lower cost.
Earning criteria — what you'll demonstrate
- Build a multilingual NLP pipeline end-to-end
- Compare open-source sentiment models to a vendor API
- Extract aspect-level signals using a fine-tuned tagger
- Communicate multilingual NLP results to non-technical hotel managers
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.
NLP Engineer
Shipping a multilingual text-mining pipeline plus dashboard is core NLP-engineer work at any vertical text-analytics vendor.
This challenge sharpens
- multilingual-nlp
- sentiment-analysis
- aspect-extraction
Data Scientist
Translating per-aspect signals into actionable hotel insights is the day-to-day of data scientists embedded with operations teams.
This challenge sharpens
- topic-modeling
- sentiment-analysis
- evaluation
AI Engineer
Wiring the dashboard prototype plus the cost case is exactly the kind of AI-engineering work consultancies hire for.
This challenge sharpens
- streamlit
- multilingual-nlp
- evaluation