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

Autor: Putu Ayulia Setiawati, I Made Agus Dwi Suarjaya, I Nyoman Prayana Trisna
Jazyk: English<br />Indonesian
Rok vydání: 2024
Předmět:
Zdroj: Journal of Information Systems and Informatics, Vol 6, Iss 2, Pp 662-675 (2024)
Druh dokumentu: article
ISSN: 2656-5935
2656-4882
DOI: 10.51519/journalisi.v6i2.713
Popis: 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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