Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner

Dayu, Wijaya and Leon A., Abdillah (2023) Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner. Journal of Data Science, 2023 (08). pp. 1-11. ISSN 2805-5160

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Abstract

The Coronavirus or other designations is COVID-19 (Corona Virus Disease) appeared in November 2019 in Wuhan, China. Over time, the virus is no longer categorized as an outbreak but is categorized as a pandemic or has spread to almost all countries in the world, including Indonesia. The emergence of COVID-19 in Indonesia in February 2020 has resulted in many sectors experiencing losses, not only in health but also in the economic sector. Recently there was a new mutation to the COVID-19 Virus, namely Omicron. Omicron has been shown to be much more infectious than the other variants with an increased ability to evade vaccines and cause re-infection. This study aims to present a result of sentiment analysis on the new variant of the COVID-19 Virus, namely Omicron which is divided into three (three) classes: positive, negative, and neutral. Then, the comments will be manually labeled followed by classification using the Nave Bayes algorithm and RapidMiner software. This study's findings revealed that 84% of the community responded positively, 7% of the community responded Neutral and 9% of the community responded negatively. It can be concluded that the community responded positively to the issue of the latest variant of the COVID-19 Omicron virus because there is also the possibility that the contents of the latest Omicron COVID-19 virus may also be dangerous from the beginning of the emergence of the COVID-19 Virus in the world.

Item Type: Article
Uncontrolled Keywords: COVID-19, Naïve Bayes, Omicron, Rapid Miner, Sentiment Analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Depositing User: Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited: 24 Aug 2023 09:11
Last Modified: 24 Aug 2023 09:11
URI: http://eprints.intimal.edu.my/id/eprint/1783

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