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

• Main Authors: Dayu, Wijaya, Leon A., Abdillah
• Format: Article
• Language: English
• Published: INTI International University 2023
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software
• Online Access: http://eprints.intimal.edu.my/1783/1/jods2023_08.pdf; http://eprints.intimal.edu.my/1783/; http://ipublishing.intimal.edu.my/jods.html
• Institution: INTI International University

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.