More specifically, this is a one dimensional "ideal point" (the author's political stance or ideological standpoint) projector that utilizes Variational Autoencoding methods to quantify an author's political leaning (the author's political position gets projected onto a one dimensional scale that ranges from moderate to progressive).
Drawing inspiration from one of the studies conducted on measuring political subjectivity and quantifying author's political stance through variational inference, this project will largely follow this paper to conduct an ad hoc analysis and unsupervised modeling over political content in the format of tweets for eliciting potential interpretable results.
A simple EDA (Exploratory Data Analysis) will also be carried out in supporting the final analysis.
Please check out this link for updates and supplementary analysis and notes. The blog will explain some important context, background, incentive, and attach axiliary notes. The major vocabs will be around understanding PACs, Super PACs, and Electoral College.
This repo serves as a sample data collection demo. (p.s. due to limited permissions of twitter API access, some of the data collected might not be as ideal).
Please also check out the references pertaining to this project. The file includes articles and resources that introduce the sufficient background in order to better understand this particular project.
Please check out this link for a final demo (this app is deployed on GCP; for cloud related content please check out this repo) . Also check out dev branch for src code and other relevant resources.
This project only explores the open tweets and data retrieved from Twitter API for personal non-commercial use. For a full collection of tweets, please email me at shiyis@brandeis.edu.