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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Budhwar, Aditya"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Foaad Khosmood"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-01T08:00:00Z","date_published":"2018-03-01T08:00:00Z","updated_at":"2026-07-24T01:31:56Z","subjects":["NLP","Machine Learning","Digital Democracy","Vote Prediction","Bill Prediction","Other Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2018.18"],"render_values":[{"text":"10.15368/theses.2018.18","href":"https://doi.org/10.15368/theses.2018.18","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1818","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Foaad Khosmood"]},{"key":"dc:creator","label":"Author","values":["Budhwar, Aditya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-05-15T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["NLP","Machine Learning","Digital Democracy","Vote Prediction","Bill Prediction","Other Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1818","10.15368/theses.2018.18"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Predicting the Vote Using Legislative Speech"]}]}],"canonical_facts":{"dc:contributor":["Foaad Khosmood"],"dc:creator":["Budhwar, Aditya"],"dc:date.available":["2018-05-15T07:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1818","10.15368/theses.2018.18"],"dc:subject":["NLP","Machine Learning","Digital Democracy","Vote Prediction","Bill Prediction","Other Computer Engineering"],"dc:title":["Predicting the Vote Using Legislative Speech"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:31:56Z"}