Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is \"For\" and the bill passes or alignment is \"Against\" and the bill fails). The process of finding what makes a testimony successful was accomplished through data filtration, feature extraction, implementation of classification models, and feature analysis. Several features were extracted and tested to find those that had the greatest impact on the bill outcome. The features chosen provided information on the sentence complexity and type of words used (adjective, verb, nouns) for each testimony. Additionally all the testimonies were analyzed to find common phrases used within successful testimonies. Two types of classification models were implemented: ones that used the manually extracted feature as input and ones that used their own feature extraction process. The results from the classification models and feature analysis show that certain aspects within a testimony such as sentence complexity and using specific phrases significantly impact the bill outcome. The most successful models, Support Vector Machine and Multinomial Naive Bayes, achieved an accuracy of 91.79\\% and 91.22\\% respectively
","abstract_html":"<p>Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is "For" and the bill passes or alignment is "Against" and the bill fails). The process of finding what makes a testimony successful was accomplished through data filtration, feature extraction, implementation of classification models, and feature analysis. Several features were extracted and tested to find those that had the greatest impact on the bill outcome. The features chosen provided information on the sentence complexity and type of words used (adjective, verb, nouns) for each testimony. Additionally all the testimonies were analyzed to find common phrases used within successful testimonies. Two types of classification models were implemented: ones that used the manually extracted feature as input and ones that used their own feature extraction process. The results from the classification models and feature analysis show that certain aspects within a testimony such as sentence complexity and using specific phrases significantly impact the bill outcome. The most successful models, Support Vector Machine and Multinomial Naive Bayes, achieved an accuracy of 91.79\\% and 91.22\\% respectively</p>","abstract_has_math":false,"creators":["Gundala, Sanjana"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Maria Pantoja","Computer Science","College of Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-01T07:00:00Z","date_published":"2022-06-01T07:00:00Z","updated_at":"2026-07-24T01:32:29Z","subjects":["Legislative Meetings","Natural Language Processing","Machine Learning","Classification Models","Testimonies","Predicting Bill Outcome","American Politics","Cognitive Science","Data Science","Other Computer Sciences","Other Linguistics","Social Work","Software Engineering","Theory and Algorithms"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2022.40"],"render_values":[{"text":"10.15368/theses.2022.40","href":"https://doi.org/10.15368/theses.2022.40","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/2468","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Maria Pantoja","Computer Science","College of Engineering"]},{"key":"dc:creator","label":"Author","values":["Gundala, Sanjana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2022-06-07T07:00:00Z"]},{"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":["Legislative Meetings","Natural Language Processing","Machine Learning","Classification Models","Testimonies","Predicting Bill Outcome","American Politics","Cognitive Science","Data Science","Other Computer Sciences","Other Linguistics","Social Work","Software Engineering","Theory and Algorithms"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/2468","10.15368/theses.2022.40"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is \"For\" and the bill passes or alignment is \"Against\" and the bill fails). The process of finding what makes a testimony successful was accomplished through data filtration, feature extraction, implementation of classification models, and feature analysis. Several features were extracted and tested to find those that had the greatest impact on the bill outcome. The features chosen provided information on the sentence complexity and type of words used (adjective, verb, nouns) for each testimony. Additionally all the testimonies were analyzed to find common phrases used within successful testimonies. Two types of classification models were implemented: ones that used the manually extracted feature as input and ones that used their own feature extraction process. The results from the classification models and feature analysis show that certain aspects within a testimony such as sentence complexity and using specific phrases significantly impact the bill outcome. The most successful models, Support Vector Machine and Multinomial Naive Bayes, achieved an accuracy of 91.79\\% and 91.22\\% respectively
"]},{"key":"dc:title","label":"Title","values":["Legislative Language for Success"]}]}],"canonical_facts":{"dc:contributor":["Maria Pantoja","Computer Science","College of Engineering"],"dc:creator":["Gundala, Sanjana"],"dc:date.available":["2022-06-07T07:00:00Z"],"dc:description.abstract":["Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is \"For\" and the bill passes or alignment is \"Against\" and the bill fails). The process of finding what makes a testimony successful was accomplished through data filtration, feature extraction, implementation of classification models, and feature analysis. Several features were extracted and tested to find those that had the greatest impact on the bill outcome. The features chosen provided information on the sentence complexity and type of words used (adjective, verb, nouns) for each testimony. Additionally all the testimonies were analyzed to find common phrases used within successful testimonies. Two types of classification models were implemented: ones that used the manually extracted feature as input and ones that used their own feature extraction process. The results from the classification models and feature analysis show that certain aspects within a testimony such as sentence complexity and using specific phrases significantly impact the bill outcome. The most successful models, Support Vector Machine and Multinomial Naive Bayes, achieved an accuracy of 91.79\\% and 91.22\\% respectively
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/2468","10.15368/theses.2022.40"],"dc:subject":["Legislative Meetings","Natural Language Processing","Machine Learning","Classification Models","Testimonies","Predicting Bill Outcome","American Politics","Cognitive Science","Data Science","Other Computer Sciences","Other Linguistics","Social Work","Software Engineering","Theory and Algorithms"],"dc:title":["Legislative Language for Success"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:32:29Z"}