More specifically, this is a one dimensional "ideal point" (the author's political stance or ideological standpoint) projector that utilizes variational encoding methods to quantify an author's political leaning (the author's political position gets projected onto a one dimensional spectrum that ranges from moderate to progressive)
Measuring Political Subjectivity with VAE
Drawing inspiration from one of the researches 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.
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 researches pertaining to this project.
Please check out this link for a final demo (this app is deployed on GCP; for information of the process 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 only. For a full collection of tweets, please email me at shiyis@brandeis.edu.