Implementation of the Support Vector Machine Method in Sentiment Analysis of Public Opinion towards the 2020 Regional Election on Twitter Social Media

  • J.B. Budi Darmawan Universitas Sanata Dharma
  • Martin Paramarta
Keywords: classification, regional elections, sentiment analysis, support vector machine, tweet

Abstract

The 2020 regional elections were postponed for several months because of the COVID-19 pandemic, until finally set to be held on December 9, 2020. Many people have opinions about the pros and cons of holding regional elections during the COVID-19 pandemic on social media, especially Twitter. This research aims to determine the number of positive and negative sentiments and the performance of the Support Vector Machine method in classifying tweet sentiments. The research data was sourced from tweets with the keyword "Pilkada 2020" which amounted to 6037 tweets. The data will be labeled positive and negative sentiment polarity automatically.    The test results showed as many as 4864 data with positive sentiment and 1173 other data with negative sentiment. In addition, the test results in this research show that the Support Vector Machine method has a fairly good performance in classifying tweet sentiment with an average accuracy result of 87.94%.

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Published
2023-11-06
How to Cite
Darmawan, J. B. and Paramarta, M. (2023) “Implementation of the Support Vector Machine Method in Sentiment Analysis of Public Opinion towards the 2020 Regional Election on Twitter Social Media”, ReTII, 18(1), pp. 836-841. Available at: //journal.itny.ac.id/index.php/ReTII/article/view/4599 (Accessed: 13June2024).