<![CDATA[The emergence of the COVID-19 pandemic has resulted in people having to always check their health, especially in determining the initial symptoms of someone being exposed to the COVID-19 virus itself. One of them is the Genose screening test as an early detection tool whether positive is exposed or negative. This has been widely discussed in the real world and in cyberspace regarding the accuracy of the genose screening test, thus giving rise to many opinions. Not only positive or negative opinions but also neutral ones. Social media, especially Twitter, is now a place to express opinions. In this study, an analysis of public tweet sentiment regarding the screening genose test was carried out as an early detection tool for the covid-19 virus by classifying responses containing positive and negative sentiments through the Twitter social network. The method used in this research is Nave Bayes Based Particle Swarm Optimization. With the process of crawling data from twitter using RapidMiner. Preprocessing the data that has been obtained from Twitter uses cleansing, tokenize, transform cases, filter tokens, stopwords, and stems. By manually assigning sentiment to the classification process.]]>
Copyrights © 2023
