2019年12月於中國大陸湖北武漢地區，發現新型冠狀病毒，隨後在2020年初迅速蔓延至全球，逐漸造成全球性的大瘟疫，被多個國際組織及新聞媒體形容是 多個國際組織及傳媒形容為自第二次世界大戰以來全球面臨的最嚴峻危機。截至2020年5月，全球已有220多個國家和地區累計報告逾471萬名確診病例，逾35萬名患者死亡。 本文於新冠肺炎全球大流行的背景，在台灣每日約有一半以上的新聞報導皆與新冠肺炎或是防疫知識相關，在本篇研究，我們利用決策樹、支援向量機、隨機森林、樸素貝氏分等分類器來對分類防疫新聞，本研究分類防疫新聞和其他新聞，對於只有兩種分類的情況下雜訊是非常嚴重對於隨機森林或是樸素貝氏的正確率會有一定的影響，實驗結果：決策樹有最好的效果(精確度：0.927)。 ;The COVID-19 pandemic, also known as the coronavirus pandemic, is an ongoing pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was first identified in Wuhan, China , in December 2019. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January, and a pandemic on 11 March. As of May 2020, more than 4.71 million cases of COVID-19 have been reported in more than 188 countries and territories, resulting in more than 315,000 deaths. More than 1.73 million people have recovered from the virus. this paper is based on the global pandemic of COVID‑19. About half of the daily news reports in Taiwan are related to COVID‑19 or epidemic prevention knowledge. This thesis studies different classification methods for the COVID-19 epidemic prevention news. Based on practical news data collected from web pages, our simulation results show that the decision tree method achieves the best classification result with an accuracy of 0.927.
