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All Journal JRSI (Jurnal Rekayasa Sistem dan Industri)
Vega Purwayoga
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Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering

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Twitter Sentiment Analysis of Recession 2023: A Comparative Study of Machine Learning Approaches Virra Retnowati A’izzah; Vega Purwayoga
Jurnal Rekayasa Sistem & Industri Vol 11 No 01 (2024): Jurnal Rekayasa Sistem & Industri
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v11i01.612

Abstract

Sentiment Analysis helps understand public opinion on a particular topic. One recent topic that has attractedattention is the potential for a global recession in 2023. In this study, five different algorithms - BernoulliNaive Bayes (BNB), Support Vector Machine (SVM), Linear Regression, K-Nearest Neighbors (KNN), andDecision Tree - were compared to determine which algorithm provided the most accurate sentiment analysisof Twitter data related to this topic. The results showed that the SVM algorithm had the highest accuracy,and most Twitter users had negative sentiments towards topics related to a potential recession in 2023, witha prediction rate of 81.7% compared to 16.3% for positive sentiments. The results of this study are expectedto be used to understand the general public's viewpoints regarding the predicted recession in 2023 and toprovide insights for developing policies and strategies to mitigate the economic downturn.
Co-Authors Virra Retnowati A’izzah