The goal of this project was to utilize Reddit data from specific subreddits in order to build language models in Python to classify political text into three categories: Repbulican, Democrat, or neutral.
For this project, two models were built, trained, and evaluated. First, a probabilistic model: we utilized a Naive Bayes text classifier from scikit-learn. Second, a neural network: this model utilized pre-trained Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art transformer architecture used for representing words. This model was implemented in PyTorch and the Huggingface transformers library.
First, in order to obtain test/train/validation data, we utilized the code in the 00_scraping folder in this repository to scrape all comments and posts from three subreddits: r/democrats, r/Republican, and r/NeutralPolitics. For this specific analysis, posts and comments were limited to those occuring in the calendar year 2020, to posts that did not link to articles or memes, and finally to posts longer than 100 characters. This resulted in approximately 800 posts per subreddit, and 4,000 comments were randomly selected (after selecting on above criteria) in each subreddit.
Full results of both models are available in the report, located in the root directory of this repository.
This project should be fully replicable, by following the workflow: 00_scraping -> 05_cleaning -> 15_code. However, the neural network should NOT be trained locally, for efficiency's sake. For this project, the neural network was trained on Google CoLab Pro.