How team9-cs435/movie-sentiment-recommender works
This repository contains a small Spark application that reads review and movie-metadata JSONL datasets, filters and reshapes verified reviews, cleans metadata, joins the datasets, and displays selected fields. The README describes sentiment-shift analysis and distributed recommendation models, but the only source file shown does not implement those capabilities, so they are not represented as runtime components. The graph reflects only the observed preparation workflow; the input dataset locations are referenced in code but are not repository files.
Main entry points: app.py Dataset join
Components 7
Other components
- Spark session
project/app.py - Review dataset
- Review cleanup
project/app.py - Metadata dataset
- Metadata cleanup
project/app.py - Dataset join
project/app.py - Sample display
project/app.py
Connections 7
- Spark session → Review dataset: reads
project/app.py - Spark session → Metadata dataset: reads
project/app.py - Review dataset → Review cleanup: filters and reshapes
project/app.py - Metadata dataset → Metadata cleanup: selects and renames
project/app.py - Review cleanup → Dataset join: joins by movie
project/app.py - Metadata cleanup → Dataset join: joins by movie
project/app.py - Dataset join → Sample display: shows sample
project/app.py