In the Fall 2024 semester, I chose to analyze reviews of vampire movies for my final project in my DACSS 758: Text as Data course, taught by Dr. Rosemary Pang at UMass Amherst. I wanted to learn about which words are commonly used by movie critics to favorably describe vampire movies and develop models to predict whether a critic's vampire movie review is positive or negative.
This repository contains 6 files:
- README.md: this file
- Vampire-Code.qmd: the R code used to build a Naive Bayes, Support Vector Machine, and Random Forest model to predict the freshness of vampire movie reviews
- Vampire-Output.html: the rendered version of the QMD file, displaying the R code, interpretations, and output
- vamp_reviews.csv: the data used to train the supervised learning models, obtained from Kaggle
- new_vamp_reviews.csv: the data used to test the models, compiled from Rotten Tomatoes
- Vampire-Poster.pdf: the digital poster summarizing the project's goals, methods, and major findings