Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp01vq27zr60r
Title: | A Text & Sentiment Analysis Approach Towards The Mexico-U.S. Border |
Authors: | Torres-Olivares, Valeria |
Advisors: | Garip, Filiz |
Department: | Princeton School of Public and International Affairs |
Certificate Program: | Latin American Studies Program |
Class Year: | 2022 |
Abstract: | This research explores the complexities of undocumented immigration and border security from three angles: survey data from the Mexican Migration Project (MMP), political rhetoric and mood from Congressional bills, Congressional hearings and New York Times articles, and enforcement data from the U.S. Customs and Border Protection (budget, arrests, and deaths). The goal is to find the connection between these three different sources of information addressing a common topic. Through this approach I can explore the potential reasons as to why the U.S. Government and policymakers continue to fund border enforcement efforts considering them not preventing undocumented migration. This approach allowed to first set the current situation of immigration policy and border enforcement to then go in and analyze potential reasons for the seemingly increasing focus on the border. Through employing these methods, I was able to determine that immigration policy and border enforcement seems to be largely tied to public sentiment (media) and is not an issue that legislators seem to care as much about. This was shown through the sentiment analysis of legislation, indicating that this policy issue is not one that seems to be as actively cared about when it comes to the formation of legislation. |
URI: | http://arks.princeton.edu/ark:/88435/dsp01vq27zr60r |
Type of Material: | Princeton University Senior Theses |
Language: | en |
Appears in Collections: | Princeton School of Public and International Affairs, 1929-2024 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
TORRES-OLIVARES-VALERIA-THESIS.pdf | 1.9 MB | Adobe PDF | Request a copy |
Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.