Covid-19 Sentiment Analysis Using Convolutional Neural Network / Reccurent Neural Network Method - Ravensca Matatula; Danny Manongga; Hendry Hendry (Eduvest - Journal of Universal Studies, 2022)
Social media is a very important tool in this modern era , one of which is namely twitter. Twitter allows user for give opinion / opinion to various issues and topics hot / viral trending . Trending on twitter is so so fast in the process of spreading so that Twitter becomes a medium of information that often become a media issue conspiracy . Covid-19 is a moderate epidemic / disease _ experienced the whole world when this . Issues circulating in the population , they believe that Covid-19 is a real pandemic and a conspiracy , issue this make population confused differentiate Among second issue that . Because of that required a fast and accurate analysis _ for produce valid results , that Covid-19 a real thing _ or conspiracy seen from opinion population and corner views written on Twitter. CNN/RNN or combined from RNN(LSTM) and CNN methods are method used _ for classify opinion population about Covid-19 issues . Study this also done with compare is correct RNN/CNN accuracy same like deep RNN even more fast for in the process . Research results state that accuracy from combined RNN/CNN no different remote , even RNN/CNN in the process more fast than deep RNNs. Research results about opinion / opinion residents on twitter who believe about Covid-19 is conspiracy more low than residents who have confidence about Covid-19 is something the real thing . Percentage classification opinion / opinion from sentiment positive by 63.15% and opinion / opinion sentiment negative by 28.60%, this is results calculation use RNN/CNN method , with accuracy reached 58%. Accuracy from method used _ make Covid-19 issues that exist in the population no Becomes hoax news so population more alert against the ongoing Covid-19 pandemic happen.