Twitter stance detection towards Job Creation Bill

Arif Hamied Nababan, Rahmad Mahendra, Indra Budi

Research output: Contribution to journalConference articlepeer-review

Abstract

The formation of Job Creation Bill has raised the polemics in Indonesia. This study aims to identify the public's stance on the Job Creation Bill on Twitter social media. We collect tweets using keywords related to the Job Creation Bill and annotate nearly 10K tweets with a class label describing stance position. The experiments were conducted using the Naïve Bayes, Support Vector Machine, and Logistic Regression, with unigram and bigram features. The best performance in our experiment achieved by the Logistic Regression classifier using the unigram feature obtains a micro F-1 score of 71.8%.

Original languageEnglish
Pages (from-to)76-81
Number of pages6
JournalProcedia Computer Science
Volume197
DOIs
Publication statusPublished - 2021
Event6th Information Systems International Conference, ISICO 2021 - Virtual, Online, Italy
Duration: 7 Aug 20218 Aug 2021

Keywords

  • Job creation bill
  • Stance detection
  • Text classification
  • Twitter

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