Predicting the Vote Using Legislative Speech
As most dedicated observers of voting bodies like the U.S. Supreme Court can attest, it is possible to guess vote outcomes based on statements made during deliberations or questioning by the voting members. In most forms of representative democracy, citizens can actively petition or lobby their repr...
Main Author: | |
---|---|
Format: | Others |
Published: |
DigitalCommons@CalPoly
2018
|
Subjects: | |
Online Access: | https://digitalcommons.calpoly.edu/theses/1818 https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=3131&context=theses |
id |
ndltd-CALPOLY-oai-digitalcommons.calpoly.edu-theses-3131 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-CALPOLY-oai-digitalcommons.calpoly.edu-theses-31312021-08-31T05:02:10Z Predicting the Vote Using Legislative Speech Budhwar, Aditya As most dedicated observers of voting bodies like the U.S. Supreme Court can attest, it is possible to guess vote outcomes based on statements made during deliberations or questioning by the voting members. In most forms of representative democracy, citizens can actively petition or lobby their representatives, and that often means understanding their intentions to vote for or against an issue of interest. In some U.S. state legislators, professional lobby groups and dedicated press members are highly informed and engaged, but the process is basically closed to ordinary citizens because they do not have enough background and familiarity with the issue, the legislator or the entire process. Our working hypothesis is that verbal utterances made during the legislative process by elected representatives can indicate their intent on a future vote, and therefore can be used to automatically predict said vote to a significant degree. In this research, we examine thousands of hours of legislative deliberations from the California state legislature’s 2015-2016 session to form models of voting behavior for each legislator and use them to train classifiers and predict the votes that occur subsequently. We can achieve legislator vote prediction accuracies as high as 83%. For bill vote prediction, our model can achieve 76% accuracy with an F1 score of 0.83 for balanced bill training data. 2018-03-01T08:00:00Z text application/pdf https://digitalcommons.calpoly.edu/theses/1818 https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=3131&context=theses Master's Theses DigitalCommons@CalPoly NLP Machine Learning Digital Democracy Vote Prediction Bill Prediction Other Computer Engineering |
collection |
NDLTD |
format |
Others
|
sources |
NDLTD |
topic |
NLP Machine Learning Digital Democracy Vote Prediction Bill Prediction Other Computer Engineering |
spellingShingle |
NLP Machine Learning Digital Democracy Vote Prediction Bill Prediction Other Computer Engineering Budhwar, Aditya Predicting the Vote Using Legislative Speech |
description |
As most dedicated observers of voting bodies like the U.S. Supreme Court can attest, it is possible to guess vote outcomes based on statements made during deliberations or questioning by the voting members. In most forms of representative democracy, citizens can actively petition or lobby their representatives, and that often means understanding their intentions to vote for or against an issue of interest. In some U.S. state legislators, professional lobby groups and dedicated press members are highly informed and engaged, but the process is basically closed to ordinary citizens because they do not have enough background and familiarity with the issue, the legislator or the entire process. Our working hypothesis is that verbal utterances made during the legislative process by elected representatives can indicate their intent on a future vote, and therefore can be used to automatically predict said vote to a significant degree. In this research, we examine thousands of hours of legislative deliberations from the California state legislature’s 2015-2016 session to form models of voting behavior for each legislator and use them to train classifiers and predict the votes that occur subsequently. We can achieve legislator vote prediction accuracies as high as 83%. For bill vote prediction, our model can achieve 76% accuracy with an F1 score of 0.83 for balanced bill training data. |
author |
Budhwar, Aditya |
author_facet |
Budhwar, Aditya |
author_sort |
Budhwar, Aditya |
title |
Predicting the Vote Using Legislative Speech |
title_short |
Predicting the Vote Using Legislative Speech |
title_full |
Predicting the Vote Using Legislative Speech |
title_fullStr |
Predicting the Vote Using Legislative Speech |
title_full_unstemmed |
Predicting the Vote Using Legislative Speech |
title_sort |
predicting the vote using legislative speech |
publisher |
DigitalCommons@CalPoly |
publishDate |
2018 |
url |
https://digitalcommons.calpoly.edu/theses/1818 https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=3131&context=theses |
work_keys_str_mv |
AT budhwaraditya predictingthevoteusinglegislativespeech |
_version_ |
1719473036717981696 |