Abstract

Every legislative candidate registered for the 2019 General Election must fill out a variety of information, including their electoral district, name, place of birth, party, occupation, status, and goals. It is crucial for motivation and goals to be made public. As a result, we use the Author-Topic Model and Node2vec to evaluate and display the themes modeling in the 2019 General Election data. Only 5.084 of the 36.889 legislative candidate records collected through data crawling from 16 parties include motive and target. While the objective topic modeling creates themes like the role of a legislative member, local development, and national movement, the motivation topic modeling generates topics like education, economics, and welfare. Several parties share similar topics in their target and motivations.

Original languageEnglish
Title of host publication2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages501-506
Number of pages6
ISBN (Electronic)9798350309225
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Lombok, Indonesia
Duration: 14 Nov 202315 Nov 2023

Publication series

Name2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings

Conference

Conference2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Country/TerritoryIndonesia
CityLombok
Period14/11/2315/11/23

Keywords

  • Author-Topic Model
  • Node2vec
  • general election

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