Research Article
Sentiment Analysis of the Job Creation Law on Twitter Data Using Support Vector Machine
@INPROCEEDINGS{10.4108/eai.28-10-2020.2315355, author={Dhevina Dewantari and Ega Javier Harwenda and Mugi Rohimah and Muhammad Ridho Kurniawan Pratama and Yova Ruldeviyani}, title={Sentiment Analysis of the Job Creation Law on Twitter Data Using Support Vector Machine}, proceedings={Proceedings of the 1st Konferensi Internasional Berbahasa Indonesia Universitas Indraprasta PGRI, KIBAR 2020, 28 October 2020, Jakarta, Indonesia}, publisher={EAI}, proceedings_a={KIBAR}, year={2022}, month={2}, keywords={public opinion sentiment analysis svm twitter uu ciptaker}, doi={10.4108/eai.28-10-2020.2315355} }- Dhevina Dewantari
Ega Javier Harwenda
Mugi Rohimah
Muhammad Ridho Kurniawan Pratama
Yova Ruldeviyani
Year: 2022
Sentiment Analysis of the Job Creation Law on Twitter Data Using Support Vector Machine
KIBAR
EAI
DOI: 10.4108/eai.28-10-2020.2315355
Abstract
Twitter is a microblogging service that allows users to share their opinions, especially on the latest public issues. The Job Creation Law (UU Ciptaker) or known as the Job Creation Omnibus Law became a public discussion on Twitter before and after its ratification. This Law amended several previous laws to create employment and increase foreign and domestic investment. Sentiment analysis was needed to analyze public opinion regarding the UU Ciptaker on Twitter, namely by classifying opinions into several classes. The aim was to provide insight to the public regarding the public's reaction to the UU Ciptaker and its effects on public opinion. Support Vector Machine (SVM) was used to classify the data. Based on the classification results of 868 tweets, 217 tweets (25%) were labeled as positive. The 651 tweets (75%) were labeled as negative. This showed that the majority of Twitter users rejected the passage of the UU Ciptaker.
