Twitter stance detection towards Job Creation Bill
Arif Hamied Nababan, Rahmad Mahendra, Indra Darmawan Budi · Procedia Computer Science · 2022
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%.