Sentiment Analysis of Unemployment in Indonesia During and Post COVID-19 on X (Twitter) Using Naïve Bayes and Support Vector Machine

Authors

  • Putu Ayulia Setiawati Udayana University, Indonesia
  • I Made Agus Dwi Suarjaya Udayana University, Indonesia
  • I Nyoman Prayana Trisna Udayana University, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v6i2.713

Keywords:

Unemployment, tweet, TF-IDF, Naïve Bayes, Support Vector Machine

Abstract

The COVID-19 pandemic has impacted health, economy, and society. Social distancing measures and quarantine policies have restricted economic activities, leading to downturns in COVID-19-affected regions and a subsequent rise in unemployment rates, particularly in urban areas. Concurrently, there has been a remarkable surge in the utilization of the X (Twitter) platform, with Indonesia ranking 6th globally in X (Twitter) users. This study aims to understand the diverse perspectives of society on unemployment and the factors influencing society's views on unemployment through sentiment analysis of X (Twitter) data. By analyzing 576,764 tweets from April 2020 to October 2023, tweets are categorized into positive, neutral, and negative classes. Classification model was built to classify tweet data by implementing TF-IDF for word weighting, and a pair of machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM). Model evaluation yielded the highest accuracy of 81.5% using Naïve Bayes. The classification outcomes highlight prevalent negative perceptions of unemployment among Indonesians, totaling 50.03%. This research contributes to the literature by providing a large-scale analysis of social media data to uncover public sentiment trends and offering insights for policymakers to address unemployment and improve welfare.

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Published

2024-06-13

Issue

Vol. 6 No. 2 (2024): June

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How to Cite

[1]
P. A. Setiawati, I. M. A. D. Suarjaya, and I. N. P. Trisna, “Sentiment Analysis of Unemployment in Indonesia During and Post COVID-19 on X (Twitter) Using Naïve Bayes and Support Vector Machine”, journalisi, vol. 6, no. 2, pp. 662–675, Jun. 2024, doi: 10.51519/journalisi.v6i2.713.

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